From fa1d18cb9d64f294b0896f06de46b4bb72e5b981 Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Sun, 28 Jul 2013 00:41:54 -0700 Subject: [PATCH 01/16] Create gh-pages branch via GitHub --- fonts/copse-regular-webfont.eot | Bin 0 -> 123680 bytes fonts/copse-regular-webfont.svg | 247 ++++ fonts/copse-regular-webfont.ttf | Bin 0 -> 123504 bytes fonts/copse-regular-webfont.woff | Bin 0 -> 46152 bytes fonts/quattrocentosans-bold-webfont.eot | Bin 0 -> 54776 bytes fonts/quattrocentosans-bold-webfont.svg | 247 ++++ fonts/quattrocentosans-bold-webfont.ttf | Bin 0 -> 54564 bytes fonts/quattrocentosans-bold-webfont.woff | Bin 0 -> 27880 bytes fonts/quattrocentosans-bolditalic-webfont.eot | Bin 0 -> 62100 bytes fonts/quattrocentosans-bolditalic-webfont.svg | 248 ++++ fonts/quattrocentosans-bolditalic-webfont.ttf | Bin 0 -> 61860 bytes .../quattrocentosans-bolditalic-webfont.woff | Bin 0 -> 31096 bytes fonts/quattrocentosans-italic-webfont.eot | Bin 0 -> 66152 bytes fonts/quattrocentosans-italic-webfont.svg | 247 ++++ fonts/quattrocentosans-italic-webfont.ttf | Bin 0 -> 65932 bytes fonts/quattrocentosans-italic-webfont.woff | Bin 0 -> 32504 bytes fonts/quattrocentosans-regular-webfont.eot | Bin 0 -> 54444 bytes fonts/quattrocentosans-regular-webfont.svg | 247 ++++ fonts/quattrocentosans-regular-webfont.ttf | Bin 0 -> 54220 bytes fonts/quattrocentosans-regular-webfont.woff | Bin 0 -> 27408 bytes images/background.png | Bin 0 -> 4559 bytes images/body-background.png | Bin 0 -> 1097 bytes images/bullet.png | Bin 0 -> 993 bytes images/hr.gif | Bin 0 -> 1349 bytes images/octocat-logo.png | Bin 0 -> 3085 bytes index.html | 44 +- javascripts/main.js | 53 + stylesheets/normalize.css | 459 +++++++ stylesheets/pygment_trac.css | 137 +- stylesheets/styles.css | 1149 +++++++++++++---- 30 files changed, 2717 insertions(+), 361 deletions(-) create mode 100644 fonts/copse-regular-webfont.eot create mode 100644 fonts/copse-regular-webfont.svg create mode 100644 fonts/copse-regular-webfont.ttf create mode 100644 fonts/copse-regular-webfont.woff create mode 100644 fonts/quattrocentosans-bold-webfont.eot create mode 100644 fonts/quattrocentosans-bold-webfont.svg create mode 100644 fonts/quattrocentosans-bold-webfont.ttf create mode 100644 fonts/quattrocentosans-bold-webfont.woff create mode 100644 fonts/quattrocentosans-bolditalic-webfont.eot create mode 100644 fonts/quattrocentosans-bolditalic-webfont.svg create mode 100644 fonts/quattrocentosans-bolditalic-webfont.ttf create mode 100644 fonts/quattrocentosans-bolditalic-webfont.woff create mode 100644 fonts/quattrocentosans-italic-webfont.eot create mode 100644 fonts/quattrocentosans-italic-webfont.svg create mode 100644 fonts/quattrocentosans-italic-webfont.ttf create mode 100644 fonts/quattrocentosans-italic-webfont.woff create mode 100644 fonts/quattrocentosans-regular-webfont.eot create mode 100644 fonts/quattrocentosans-regular-webfont.svg create mode 100644 fonts/quattrocentosans-regular-webfont.ttf create mode 100644 fonts/quattrocentosans-regular-webfont.woff create mode 100644 images/background.png create mode 100644 images/body-background.png create mode 100644 images/bullet.png create mode 100644 images/hr.gif create mode 100644 images/octocat-logo.png create mode 100644 javascripts/main.js create mode 100644 stylesheets/normalize.css diff --git a/fonts/copse-regular-webfont.eot b/fonts/copse-regular-webfont.eot new file mode 100644 index 0000000000000000000000000000000000000000..af1f5e6e27f634cda8e2f704ddfdd2f9e3f439bc GIT binary patch literal 123680 zcmeFa33yfIwg10&W=_VFfrOBR43Gqp5D0+~2oMmNK}0|%Db}fa#d6g;wAxl=P*lKK z8N>+(6njpHNI?Xott~jTDgl8a2q;+5Ac(zHbN-*TlUS86_ul@0_xIfA|2+SM59ef^ zefEC$de=Md_gyRZpzF-~z;#?F%(0&^Co(+7ULss)v1^i1W5XTi*Y>XU#}o6%(g(jR zZI%7=)#se${M4D{T;p7VFu@te>t)W*oGYCRovV>#Iunsx&F7bM+*MAl)7|Onbn|}g z(|>+D_q6Zwb-Q>n|3x^tLxzkWJ>&Y34G1kASwjb(J;Y7t6UQCK-?>A_o^xKp*K51+ 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-
-

LearnDataScience

-

Open content for self-directed learning in data science

- +
+

LearnDataScience

+

Open content for self-directed learning in data science

+
-

This project is maintained by nborwankar

+ -
+
+

Learn Data Science

@@ -124,10 +133,11 @@

- + \ No newline at end of file diff --git a/javascripts/main.js b/javascripts/main.js new file mode 100644 index 0000000..c57e54c --- /dev/null +++ b/javascripts/main.js @@ -0,0 +1,53 @@ +var sectionHeight = function() { + var total = $(window).height(), + $section = $('section').css('height','auto'); + + if ($section.outerHeight(true) < total) { + var margin = $section.outerHeight(true) - $section.height(); + $section.height(total - margin - 20); + } else { + $section.css('height','auto'); + } +} + +$(window).resize(sectionHeight); + +$(document).ready(function(){ + $("section h1, section h2").each(function(){ + $("nav ul").append("
  • " + $(this).text() + "
    • "); + $(this).attr("id",$(this).text().toLowerCase().replace(/ /g, '-').replace(/[^\w-]+/g,'')); + $("nav ul li:first-child a").parent().addClass("active"); + }); + + $("nav ul li").on("click", "a", function(event) { + var position = $($(this).attr("href")).offset().top - 190; + $("html, body").animate({scrollTop: position}, 400); + $("nav ul li a").parent().removeClass("active"); + $(this).parent().addClass("active"); + event.preventDefault(); + }); + + sectionHeight(); + + $('img').load(sectionHeight); +}); + +fixScale = function(doc) { + + var addEvent = 'addEventListener', + type = 'gesturestart', + qsa = 'querySelectorAll', + scales = [1, 1], + meta = qsa in doc ? doc[qsa]('meta[name=viewport]') : []; + + function fix() { + meta.content = 'width=device-width,minimum-scale=' + scales[0] + ',maximum-scale=' + scales[1]; + doc.removeEventListener(type, fix, true); + } + + if ((meta = meta[meta.length - 1]) && addEvent in doc) { + fix(); + scales = [.25, 1.6]; + doc[addEvent](type, fix, true); + } +}; \ No newline at end of file diff --git a/stylesheets/normalize.css b/stylesheets/normalize.css new file mode 100644 index 0000000..bc2ba93 --- /dev/null +++ b/stylesheets/normalize.css @@ -0,0 +1,459 @@ +/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */ +/* ============================================================================= + HTML5 display definitions + ========================================================================== */ +/* + * Corrects block display not defined in IE6/7/8/9 & FF3 + */ +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +nav, +section, +summary { + display: block; +} + +/* + * Corrects inline-block display not defined in IE6/7/8/9 & FF3 + */ +audio, +canvas, +video { + display: inline-block; + *display: inline; + *zoom: 1; +} + +/* + * Prevents modern browsers from displaying 'audio' without controls + */ +audio:not([controls]) { + display: none; +} + +/* + * Addresses styling for 'hidden' attribute not present in IE7/8/9, FF3, S4 + * Known issue: no IE6 support + */ +[hidden] { + display: none; +} + +/* ============================================================================= + Base + ========================================================================== */ +/* + * 1. Corrects text resizing oddly in IE6/7 when body font-size is set using em units + * http://clagnut.com/blog/348/#c790 + * 2. Prevents iOS text size adjust after orientation change, without disabling user zoom + * www.456bereastreet.com/archive/201012/controlling_text_size_in_safari_for_ios_without_disabling_user_zoom/ + */ +html { + font-size: 100%; + /* 1 */ + -webkit-text-size-adjust: 100%; + /* 2 */ + -ms-text-size-adjust: 100%; + /* 2 */ +} + +/* + * Addresses font-family inconsistency between 'textarea' and other form elements. + */ +html, +button, +input, +select, +textarea { + font-family: sans-serif; +} + +/* + * Addresses margins handled incorrectly in IE6/7 + */ +body { + margin: 0; +} + +/* ============================================================================= + Links + ========================================================================== */ +/* + * Addresses outline displayed oddly in Chrome + */ +a:focus { + outline: thin dotted; +} + +/* + * Improves readability when focused and also mouse hovered in all browsers + * people.opera.com/patrickl/experiments/keyboard/test + */ +a:hover, +a:active { + outline: 0; +} + +/* ============================================================================= + Typography + ========================================================================== */ +/* + * Addresses font sizes and margins set differently in IE6/7 + * Addresses font sizes within 'section' and 'article' in FF4+, Chrome, S5 + */ +h1 { + font-size: 2em; + margin: 0.67em 0; +} + +h2 { + font-size: 1.5em; + margin: 0.83em 0; +} + +h3 { + font-size: 1.17em; + margin: 1em 0; +} + +h4 { + font-size: 1em; + margin: 1.33em 0; +} + +h5 { + font-size: 0.83em; + margin: 1.67em 0; +} + +h6 { + font-size: 0.75em; + margin: 2.33em 0; +} + +/* + * Addresses styling not present in IE7/8/9, S5, Chrome + */ +abbr[title] { + border-bottom: 1px dotted; +} + +/* + * Addresses style set to 'bolder' in FF3+, S4/5, Chrome +*/ +b, +strong { + font-weight: bold; +} + +blockquote { + margin: 1em 40px; +} + +/* + * Addresses styling not present in S5, Chrome + */ +dfn { + font-style: italic; +} + +/* + * Addresses styling not present in IE6/7/8/9 + */ +mark { + background: #ff0; + color: #000; +} + +/* + * Addresses margins set differently in IE6/7 + */ +p, +pre { + margin: 1em 0; +} + +/* + * Corrects font family set oddly in IE6, S4/5, Chrome + * en.wikipedia.org/wiki/User:Davidgothberg/Test59 + */ +pre, +code, +kbd, +samp { + font-family: monospace, serif; + _font-family: 'courier new', monospace; + font-size: 1em; +} + +/* + * 1. Addresses CSS quotes not supported in IE6/7 + * 2. Addresses quote property not supported in S4 + */ +/* 1 */ +q { + quotes: none; +} + +/* 2 */ +q:before, +q:after { + content: ''; + content: none; +} + +small { + font-size: 75%; +} + +/* + * Prevents sub and sup affecting line-height in all browsers + * gist.github.com/413930 + */ +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; +} + +sup { + top: -0.5em; +} + +sub { + bottom: -0.25em; +} + +/* ============================================================================= + Lists + ========================================================================== */ +/* + * Addresses margins set differently in IE6/7 + */ +dl, +menu, +ol, +ul { + margin: 1em 0; +} + +dd { + margin: 0 0 0 40px; +} + +/* + * Addresses paddings set differently in IE6/7 + */ +menu, +ol, +ul { + padding: 0 0 0 40px; +} + +/* + * Corrects list images handled incorrectly in IE7 + */ +nav ul, +nav ol { + list-style: none; + list-style-image: none; +} + +/* ============================================================================= + Embedded content + ========================================================================== */ +/* + * 1. Removes border when inside 'a' element in IE6/7/8/9, FF3 + * 2. Improves image quality when scaled in IE7 + * code.flickr.com/blog/2008/11/12/on-ui-quality-the-little-things-client-side-image-resizing/ + */ +img { + border: 0; + /* 1 */ + -ms-interpolation-mode: bicubic; + /* 2 */ +} + +/* + * Corrects overflow displayed oddly in IE9 + */ +svg:not(:root) { + overflow: hidden; +} + +/* ============================================================================= + Figures + ========================================================================== */ +/* + * Addresses margin not present in IE6/7/8/9, S5, O11 + */ +figure { + margin: 0; +} + +/* ============================================================================= + Forms + ========================================================================== */ +/* + * Corrects margin displayed oddly in IE6/7 + */ +form { + margin: 0; +} + +/* + * Define consistent border, margin, and padding + */ +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} + +/* + * 1. Corrects color not being inherited in IE6/7/8/9 + * 2. Corrects text not wrapping in FF3 + * 3. Corrects alignment displayed oddly in IE6/7 + */ +legend { + border: 0; + /* 1 */ + padding: 0; + white-space: normal; + /* 2 */ + *margin-left: -7px; + /* 3 */ +} + +/* + * 1. Corrects font size not being inherited in all browsers + * 2. Addresses margins set differently in IE6/7, FF3+, S5, Chrome + * 3. Improves appearance and consistency in all browsers + */ +button, +input, +select, +textarea { + font-size: 100%; + /* 1 */ + margin: 0; + /* 2 */ + vertical-align: baseline; + /* 3 */ + *vertical-align: middle; + /* 3 */ +} + +/* + * Addresses FF3/4 setting line-height on 'input' using !important in the UA stylesheet + */ +button, +input { + line-height: normal; + /* 1 */ +} + +/* + * 1. Improves usability and consistency of cursor style between image-type 'input' and others + * 2. Corrects inability to style clickable 'input' types in iOS + * 3. Removes inner spacing in IE7 without affecting normal text inputs + * Known issue: inner spacing remains in IE6 + */ +button, +input[type="button"], +input[type="reset"], +input[type="submit"] { + cursor: pointer; + /* 1 */ + -webkit-appearance: button; + /* 2 */ + *overflow: visible; + /* 3 */ +} + +/* + * Re-set default cursor for disabled elements + */ +button[disabled], +input[disabled] { + cursor: default; +} + +/* + * 1. Addresses box sizing set to content-box in IE8/9 + * 2. Removes excess padding in IE8/9 + * 3. Removes excess padding in IE7 + Known issue: excess padding remains in IE6 + */ +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + /* 1 */ + padding: 0; + /* 2 */ + *height: 13px; + /* 3 */ + *width: 13px; + /* 3 */ +} + +/* + * 1. Addresses appearance set to searchfield in S5, Chrome + * 2. Addresses box-sizing set to border-box in S5, Chrome (include -moz to future-proof) + */ +input[type="search"] { + -webkit-appearance: textfield; + /* 1 */ + -moz-box-sizing: content-box; + -webkit-box-sizing: content-box; + /* 2 */ + box-sizing: content-box; +} + +/* + * Removes inner padding and search cancel button in S5, Chrome on OS X + */ +input[type="search"]::-webkit-search-decoration, +input[type="search"]::-webkit-search-cancel-button { + -webkit-appearance: none; +} + +/* + * Removes inner padding and border in FF3+ + * www.sitepen.com/blog/2008/05/14/the-devils-in-the-details-fixing-dojos-toolbar-buttons/ + */ +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} + +/* + * 1. Removes default vertical scrollbar in IE6/7/8/9 + * 2. Improves readability and alignment in all browsers + */ +textarea { + overflow: auto; + /* 1 */ + vertical-align: top; + /* 2 */ +} + +/* ============================================================================= + Tables + ========================================================================== */ +/* + * Remove most spacing between table cells + */ +table { + border-collapse: collapse; + border-spacing: 0; +} diff --git a/stylesheets/pygment_trac.css b/stylesheets/pygment_trac.css index c6a6452..62fd970 100644 --- a/stylesheets/pygment_trac.css +++ b/stylesheets/pygment_trac.css @@ -1,69 +1,70 @@ -.highlight { background: #ffffff; } -.highlight .c { color: #999988; font-style: italic } /* Comment */ +.highlight .hll { background-color: #404040 } +.highlight { color: #d0d0d0 } +.highlight .c { color: #999999; font-style: italic } /* Comment */ .highlight .err { color: #a61717; background-color: #e3d2d2 } /* Error */ -.highlight .k { font-weight: bold } /* Keyword */ -.highlight .o { font-weight: bold } /* Operator */ -.highlight .cm { color: #999988; 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- -/* MeyerWeb Reset */ - -html, body, div, span, applet, object, iframe, -h1, h2, h3, h4, h5, h6, p, blockquote, pre, -a, abbr, acronym, address, big, cite, code, -del, dfn, em, img, ins, kbd, q, s, samp, -small, strike, strong, sub, sup, tt, var, -b, u, i, center, -dl, dt, dd, ol, ul, li, -fieldset, form, label, legend, -table, caption, tbody, tfoot, thead, tr, th, td, -article, aside, canvas, details, embed, -figure, figcaption, footer, header, hgroup, -menu, nav, output, ruby, section, summary, -time, mark, audio, video { - margin: 0; - padding: 0; - border: 0; - font: inherit; - vertical-align: baseline; -} - - -/* Base text styles */ - -body { - padding:10px 50px 0 0; - font-family:"Helvetica Neue", Helvetica, Arial, sans-serif; - font-size: 14px; - color: #232323; - background-color: #FBFAF7; - margin: 0; - line-height: 1.8em; - -webkit-font-smoothing: antialiased; - -} - -h1, h2, h3, h4, h5, h6 { - color:#232323; - margin:36px 0 10px; +/* +Leap Day for GitHub Pages +by Matt Graham +*/ +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-bold-webfont.eot"); + src: url("../fonts/quattrocentosans-bold-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-bold-webfont.woff") format("woff"), url("../fonts/quattrocentosans-bold-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-bold-webfont.svg#QuattrocentoSansBold") format("svg"); + font-weight: bold; + font-style: normal; } -p, ul, ol, table, dl { - margin:0 0 22px; +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-bolditalic-webfont.eot"); + src: url("../fonts/quattrocentosans-bolditalic-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-bolditalic-webfont.woff") format("woff"), url("../fonts/quattrocentosans-bolditalic-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-bolditalic-webfont.svg#QuattrocentoSansBoldItalic") format("svg"); + font-weight: bold; + font-style: italic; } -h1, h2, h3 { - font-family: Arvo, Monaco, serif; - line-height:1.3; - font-weight: normal; +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-italic-webfont.eot"); + src: url("../fonts/quattrocentosans-italic-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-italic-webfont.woff") format("woff"), url("../fonts/quattrocentosans-italic-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-italic-webfont.svg#QuattrocentoSansItalic") format("svg"); + font-weight: normal; + font-style: italic; } -h1,h2, h3 { - display: block; - border-bottom: 1px solid #ccc; - padding-bottom: 5px; +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-regular-webfont.eot"); + src: url("../fonts/quattrocentosans-regular-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-regular-webfont.woff") format("woff"), url("../fonts/quattrocentosans-regular-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-regular-webfont.svg#QuattrocentoSansRegular") format("svg"); + font-weight: normal; + font-style: normal; +} + +@font-face { + font-family: 'Copse'; + src: url("../fonts/copse-regular-webfont.eot"); + src: url("../fonts/copse-regular-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/copse-regular-webfont.woff") format("woff"), url("../fonts/copse-regular-webfont.ttf") format("truetype"), url("../fonts/copse-regular-webfont.svg#CopseRegular") format("svg"); + font-weight: normal; + font-style: normal; +} + +/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */ +/* ============================================================================= + HTML5 display definitions + ========================================================================== */ +/* + * Corrects block display not defined in IE6/7/8/9 & FF3 + */ +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +nav, +section, +summary { + display: block; +} + +/* + * Corrects inline-block display not defined in IE6/7/8/9 & FF3 + */ +audio, +canvas, +video { + display: inline-block; + *display: inline; + *zoom: 1; +} + +/* + * Prevents modern browsers from displaying 'audio' without controls + */ +audio:not([controls]) { + display: none; +} + +/* + * Addresses styling for 'hidden' attribute not present in IE7/8/9, FF3, S4 + * Known issue: no IE6 support + */ +[hidden] { + display: none; +} + +/* ============================================================================= + Base + ========================================================================== */ +/* + * 1. Corrects text resizing oddly in IE6/7 when body font-size is set using em units + * http://clagnut.com/blog/348/#c790 + * 2. Prevents iOS text size adjust after orientation change, without disabling user zoom + * www.456bereastreet.com/archive/201012/controlling_text_size_in_safari_for_ios_without_disabling_user_zoom/ + */ +html { + font-size: 100%; + /* 1 */ + -webkit-text-size-adjust: 100%; + /* 2 */ + -ms-text-size-adjust: 100%; + /* 2 */ +} + +/* + * Addresses font-family inconsistency between 'textarea' and other form elements. + */ +html, +button, +input, +select, +textarea { + font-family: sans-serif; +} + +/* + * Addresses margins handled incorrectly in IE6/7 + */ +body { + margin: 0; } +/* ============================================================================= + Links + ========================================================================== */ +/* + * Addresses outline displayed oddly in Chrome + */ +a:focus { + outline: thin dotted; +} + +/* + * Improves readability when focused and also mouse hovered in all browsers + * people.opera.com/patrickl/experiments/keyboard/test + */ +a:hover, +a:active { + outline: 0; +} + +/* ============================================================================= + Typography + ========================================================================== */ +/* + * Addresses font sizes and margins set differently in IE6/7 + * Addresses font sizes within 'section' and 'article' in FF4+, Chrome, S5 + */ h1 { - font-size: 30px; + font-size: 2em; + margin: 0.67em 0; } h2 { - font-size: 24px; + font-size: 1.5em; + margin: 0.83em 0; } h3 { - font-size: 18px; + font-size: 1.17em; + margin: 1em 0; } -h4, h5, h6 { - font-family: Arvo, Monaco, serif; - font-weight: 700; +h4 { + font-size: 1em; + margin: 1.33em 0; } -a { - color:#C30000; - font-weight:200; - text-decoration:none; +h5 { + font-size: 0.83em; + margin: 1.67em 0; } -a:hover { - text-decoration: underline; +h6 { + font-size: 0.75em; + margin: 2.33em 0; } -a small { - font-size: 12px; +/* + * Addresses styling not present in IE7/8/9, S5, Chrome + */ +abbr[title] { + border-bottom: 1px dotted; } -em { - font-style: italic; +/* + * Addresses style set to 'bolder' in FF3+, S4/5, Chrome +*/ +b, +strong { + font-weight: bold; } -strong { - font-weight:700; +blockquote { + margin: 1em 40px; } -ul li { - list-style: inside; - padding-left: 25px; +/* + * Addresses styling not present in S5, Chrome + */ +dfn { + font-style: italic; } -ol li { - list-style: decimal inside; - padding-left: 20px; +/* + * Addresses styling not present in IE6/7/8/9 + */ +mark { + background: #ff0; + color: #000; } -blockquote { - margin: 0; - padding: 0 0 0 20px; - font-style: italic; +/* + * Addresses margins set differently in IE6/7 + */ +p, +pre { + margin: 1em 0; +} + +/* + * Corrects font family set oddly in IE6, S4/5, Chrome + * en.wikipedia.org/wiki/User:Davidgothberg/Test59 + */ +pre, +code, +kbd, +samp { + font-family: monospace, serif; + _font-family: 'courier new', monospace; + font-size: 1em; +} + +/* + * 1. Addresses CSS quotes not supported in IE6/7 + * 2. Addresses quote property not supported in S4 + */ +/* 1 */ +q { + quotes: none; +} + +/* 2 */ +q:before, +q:after { + content: ''; + content: none; +} + +small { + font-size: 75%; +} + +/* + * Prevents sub and sup affecting line-height in all browsers + * gist.github.com/413930 + */ +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; } -dl, dt, dd, dl p { - font-color: #444; +sup { + top: -0.5em; } -dl dt { - font-weight: bold; +sub { + bottom: -0.25em; } -dl dd { - padding-left: 20px; - font-style: italic; +/* ============================================================================= + Lists + ========================================================================== */ +/* + * Addresses margins set differently in IE6/7 + */ +dl, +menu, +ol, +ul { + margin: 1em 0; } -dl p { - padding-left: 20px; - font-style: italic; +dd { + margin: 0 0 0 40px; } -hr { - border:0; - background:#ccc; - height:1px; - margin:0 0 24px; +/* + * Addresses paddings set differently in IE6/7 + */ +menu, +ol, +ul { + padding: 0 0 0 40px; } -/* Images */ +/* + * Corrects list images handled incorrectly in IE7 + */ +nav ul, +nav ol { + list-style: none; + list-style-image: none; +} +/* ============================================================================= + Embedded content + ========================================================================== */ +/* + * 1. Removes border when inside 'a' element in IE6/7/8/9, FF3 + * 2. Improves image quality when scaled in IE7 + * code.flickr.com/blog/2008/11/12/on-ui-quality-the-little-things-client-side-image-resizing/ + */ img { - position: relative; - margin: 0 auto; - max-width: 650px; - padding: 5px; - margin: 10px 0 32px 0; - border: 1px solid #ccc; + border: 0; + /* 1 */ + -ms-interpolation-mode: bicubic; + /* 2 */ } +/* + * Corrects overflow displayed oddly in IE9 + */ +svg:not(:root) { + overflow: hidden; +} -/* Code blocks */ - -code, pre { - font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal, monospace; - color:#000; - font-size:14px; +/* ============================================================================= + Figures + ========================================================================== */ +/* + * Addresses margin not present in IE6/7/8/9, S5, O11 + */ +figure { + margin: 0; } -pre { - padding: 4px 12px; - background: #FDFEFB; - border-radius:4px; - border:1px solid #D7D8C8; - overflow: auto; - overflow-y: hidden; - margin-bottom: 32px; +/* ============================================================================= + Forms + ========================================================================== */ +/* + * Corrects margin displayed oddly in IE6/7 + */ +form { + margin: 0; } +/* + * Define consistent border, margin, and padding + */ +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} -/* Tables */ +/* + * 1. Corrects color not being inherited in IE6/7/8/9 + * 2. Corrects text not wrapping in FF3 + * 3. Corrects alignment displayed oddly in IE6/7 + */ +legend { + border: 0; + /* 1 */ + padding: 0; + white-space: normal; + /* 2 */ + *margin-left: -7px; + /* 3 */ +} + +/* + * 1. Corrects font size not being inherited in all browsers + * 2. Addresses margins set differently in IE6/7, FF3+, S5, Chrome + * 3. Improves appearance and consistency in all browsers + */ +button, +input, +select, +textarea { + font-size: 100%; + /* 1 */ + margin: 0; + /* 2 */ + vertical-align: baseline; + /* 3 */ + *vertical-align: middle; + /* 3 */ +} + +/* + * Addresses FF3/4 setting line-height on 'input' using !important in the UA stylesheet + */ +button, +input { + line-height: normal; + /* 1 */ +} + +/* + * 1. Improves usability and consistency of cursor style between image-type 'input' and others + * 2. Corrects inability to style clickable 'input' types in iOS + * 3. Removes inner spacing in IE7 without affecting normal text inputs + * Known issue: inner spacing remains in IE6 + */ +button, +input[type="button"], +input[type="reset"], +input[type="submit"] { + cursor: pointer; + /* 1 */ + -webkit-appearance: button; + /* 2 */ + *overflow: visible; + /* 3 */ +} + +/* + * Re-set default cursor for disabled elements + */ +button[disabled], +input[disabled] { + cursor: default; +} + +/* + * 1. Addresses box sizing set to content-box in IE8/9 + * 2. Removes excess padding in IE8/9 + * 3. Removes excess padding in IE7 + Known issue: excess padding remains in IE6 + */ +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + /* 1 */ + padding: 0; + /* 2 */ + *height: 13px; + /* 3 */ + *width: 13px; + /* 3 */ +} + +/* + * 1. Addresses appearance set to searchfield in S5, Chrome + * 2. Addresses box-sizing set to border-box in S5, Chrome (include -moz to future-proof) + */ +input[type="search"] { + -webkit-appearance: textfield; + /* 1 */ + -moz-box-sizing: content-box; + -webkit-box-sizing: content-box; + /* 2 */ + box-sizing: content-box; +} + +/* + * Removes inner padding and search cancel button in S5, Chrome on OS X + */ +input[type="search"]::-webkit-search-decoration, +input[type="search"]::-webkit-search-cancel-button { + -webkit-appearance: none; +} + +/* + * Removes inner padding and border in FF3+ + * www.sitepen.com/blog/2008/05/14/the-devils-in-the-details-fixing-dojos-toolbar-buttons/ + */ +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} -table { - width:100%; +/* + * 1. Removes default vertical scrollbar in IE6/7/8/9 + * 2. Improves readability and alignment in all browsers + */ +textarea { + overflow: auto; + /* 1 */ + vertical-align: top; + /* 2 */ } +/* ============================================================================= + Tables + ========================================================================== */ +/* + * Remove most spacing between table cells + */ table { - border: 1px solid #ccc; - margin-bottom: 32px; - text-align: left; - } - -th { - font-family: 'Arvo', Helvetica, Arial, sans-serif; - font-size: 18px; - font-weight: normal; - padding: 10px; - background: #232323; - color: #FDFEFB; - } - -td { - padding: 10px; - background: #ccc; - } + border-collapse: collapse; + border-spacing: 0; +} +body { + font: 14px/22px "Quattrocento Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; + color: #666; + font-weight: 300; + margin: 0px; + padding: 0px 0 20px 0px; + background: url(../images/body-background.png) #eae6d1; +} -/* Wrapper */ -.wrapper { - width:960px; +h1, h2, h3, h4, h5, h6 { + color: #333; + margin: 0 0 10px; } +p, ul, ol, table, pre, dl { + margin: 0 0 20px; +} -/* Header */ +h1, h2, h3 { + line-height: 1.1; +} -header { - background-color: #171717; - color: #FDFDFB; - width:170px; - float:left; - position:fixed; - border: 1px solid #000; - -webkit-border-top-right-radius: 4px; - -webkit-border-bottom-right-radius: 4px; - -moz-border-radius-topright: 4px; - -moz-border-radius-bottomright: 4px; - border-top-right-radius: 4px; - border-bottom-right-radius: 4px; - padding: 34px 25px 22px 50px; - margin: 30px 25px 0 0; - -webkit-font-smoothing: antialiased; +h1 { + font-size: 28px; } -p.header { - font-size: 16px; +h2 { + font-size: 24px; + color: #393939; } -h1.header { - font-family: Arvo, sans-serif; - font-size: 30px; - font-weight: 300; - line-height: 1.3em; - border-bottom: none; - margin-top: 0; +h3, h4, h5, h6 { + color: #666666; } +h3 { + font-size: 18px; + line-height: 24px; +} -h1.header, a.header, a.name, header a{ - color: #fff; +a { + color: #3399cc; + font-weight: 400; + text-decoration: none; } -a.header { - text-decoration: underline; +a small { + font-size: 11px; + color: #666; + margin-top: -0.6em; + display: block; } -a.name { - white-space: nowrap; +ul { + list-style-image: url("../images/bullet.png"); } -header ul { - list-style:none; - padding:0; +strong { + font-weight: bold; + color: #333; } -header li { - list-style-type: none; - width:132px; - height:15px; - margin-bottom: 12px; - line-height: 1em; - padding: 6px 6px 6px 7px; +.wrapper { + width: 650px; + margin: 0 auto; + position: relative; +} - background: #AF0011; - background: -moz-linear-gradient(top, #AF0011 0%, #820011 100%); - background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); - background: -webkit-linear-gradient(top, #AF0011 0%,#820011 100%); - background: -o-linear-gradient(top, #AF0011 0%,#820011 100%); - background: -ms-linear-gradient(top, #AF0011 0%,#820011 100%); - background: linear-gradient(top, #AF0011 0%,#820011 100%); +section img { + max-width: 100%; +} - border-radius:4px; - border:1px solid #0D0D0D; +blockquote { + border-left: 1px solid #ffcc00; + margin: 0; + padding: 0 0 0 20px; + font-style: italic; +} - -webkit-box-shadow: inset 0px 1px 1px 0 rgba(233,2,38, 1); - box-shadow: inset 0px 1px 1px 0 rgba(233,2,38, 1); +code { + font-family: "Lucida Sans", Monaco, Bitstream Vera Sans Mono, Lucida Console, Terminal; + font-size: 13px; + color: #efefef; + text-shadow: 0px 1px 0px #000; + margin: 0 4px; + padding: 2px 6px; + background: #333; + -moz-border-radius: 2px; + -webkit-border-radius: 2px; + -o-border-radius: 2px; + -ms-border-radius: 2px; + -khtml-border-radius: 2px; + border-radius: 2px; +} +pre { + padding: 8px 15px; + background: #333333; + -moz-border-radius: 3px; + -webkit-border-radius: 3px; + -o-border-radius: 3px; + -ms-border-radius: 3px; + -khtml-border-radius: 3px; + border-radius: 3px; + border: 1px solid #c7c7c7; + overflow: auto; + overflow-y: hidden; +} +pre code { + margin: 0px; + padding: 0px; } -header li:hover { - background: #C3001D; - background: -moz-linear-gradient(top, #C3001D 0%, #950119 100%); - background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); - background: -webkit-linear-gradient(top, #C3001D 0%,#950119 100%); - background: -o-linear-gradient(top, #C3001D 0%,#950119 100%); - background: -ms-linear-gradient(top, #C3001D 0%,#950119 100%); - background: linear-gradient(top, #C3001D 0%,#950119 100%); +table { + width: 100%; + border-collapse: collapse; } -a.buttons { - -webkit-font-smoothing: antialiased; - background: url(../images/arrow-down.png) no-repeat; - font-weight: normal; - text-shadow: rgba(0, 0, 0, 0.4) 0 -1px 0; - padding: 2px 2px 2px 22px; - height: 30px; +th { + text-align: left; + padding: 5px 10px; + border-bottom: 1px solid #e5e5e5; + color: #444; } -a.github { - background: url(../images/octocat-small.png) no-repeat 1px; +td { + text-align: left; + padding: 5px 10px; + border-bottom: 1px solid #e5e5e5; + border-right: 1px solid #ffcc00; +} +td:first-child { + border-left: 1px solid #ffcc00; } -a.buttons:hover { - color: #fff; - text-decoration: none; +hr { + border: 0; + outline: none; + height: 11px; + background: transparent url("../images/hr.gif") center center repeat-x; + margin: 0 0 20px; } +dt { + color: #444; + font-weight: 700; +} -/* Section - for main page content */ +header { + padding: 25px 20px 40px 20px; + margin: 0; + position: fixed; + top: 0; + left: 0; + right: 0; + width: 100%; + text-align: center; + background: url(../images/background.png) #4276b6; + -moz-box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + -webkit-box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + -o-box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + z-index: 99; + -webkit-font-smoothing: antialiased; + min-height: 76px; +} +header h1 { + font: 40px/48px "Copse", "Helvetica Neue", Helvetica, Arial, sans-serif; + color: #f3f3f3; + text-shadow: 0px 2px 0px #235796; + margin: 0px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; +} +header p { + color: #d8d8d8; + text-shadow: rgba(0, 0, 0, 0.2) 0 1px 0; + font-size: 18px; + margin: 0px; +} + +#banner { + z-index: 100; + left: 0; + right: 50%; + height: 50px; + margin-right: -382px; + position: fixed; + top: 115px; + background: #ffcc00; + border: 1px solid #f0b500; + -moz-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + -webkit-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + -o-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + -moz-border-radius: 0px 2px 2px 0px; + -webkit-border-radius: 0px 2px 2px 0px; + -o-border-radius: 0px 2px 2px 0px; + -ms-border-radius: 0px 2px 2px 0px; + -khtml-border-radius: 0px 2px 2px 0px; + border-radius: 0px 2px 2px 0px; + padding-right: 10px; +} +#banner .button { + border: 1px solid #dba500; + background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #ffe788), color-stop(100%, #ffce38)); + background: -webkit-linear-gradient(#ffe788, #ffce38); + background: -moz-linear-gradient(#ffe788, #ffce38); + background: -o-linear-gradient(#ffe788, #ffce38); + background: -ms-linear-gradient(#ffe788, #ffce38); + background: linear-gradient(#ffe788, #ffce38); + -moz-border-radius: 2px; + -webkit-border-radius: 2px; + -o-border-radius: 2px; + -ms-border-radius: 2px; + -khtml-border-radius: 2px; + border-radius: 2px; + -moz-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + -webkit-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + -o-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + background-color: #FFE788; + margin-left: 5px; + padding: 10px 12px; + margin-top: 6px; + line-height: 14px; + font-size: 14px; + color: #333; + font-weight: bold; + display: inline-block; + text-align: center; +} +#banner .button:hover { + background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #ffe788), color-stop(100%, #ffe788)); + background: -webkit-linear-gradient(#ffe788, #ffe788); + background: -moz-linear-gradient(#ffe788, #ffe788); + background: -o-linear-gradient(#ffe788, #ffe788); + background: -ms-linear-gradient(#ffe788, #ffe788); + background: linear-gradient(#ffe788, #ffe788); + background-color: #ffeca0; +} +#banner .fork { + position: fixed; + left: 50%; + margin-left: -325px; + padding: 10px 12px; + margin-top: 6px; + line-height: 14px; + font-size: 14px; + background-color: #FFE788; +} +#banner .downloads { + float: right; + margin: 0 45px 0 0; +} +#banner .downloads span { + float: left; + line-height: 52px; + font-size: 90%; + color: #9d7f0d; + text-transform: uppercase; + text-shadow: rgba(255, 255, 255, 0.2) 0 1px 0; +} +#banner ul { + list-style: none; + height: 40px; + padding: 0; + float: left; + margin-left: 10px; +} +#banner ul li { + display: inline; +} +#banner ul li a.button { + background-color: #FFE788; +} +#banner #logo { + position: absolute; + height: 36px; + width: 36px; + right: 7px; + top: 7px; + display: block; + background: url(../images/octocat-logo.png); +} section { - width:650px; - float:right; - padding-bottom:50px; + width: 590px; + padding: 30px 30px 50px 30px; + margin: 20px 0; + margin-top: 190px; + position: relative; + background: #fbfbfb; + -moz-border-radius: 3px; + -webkit-border-radius: 3px; + -o-border-radius: 3px; + -ms-border-radius: 3px; + -khtml-border-radius: 3px; + border-radius: 3px; + border: 1px solid #cbcbcb; + -moz-box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); + -webkit-box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); + -o-box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); + box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); +} + +small { + font-size: 12px; +} + +nav { + width: 230px; + position: fixed; + top: 220px; + left: 50%; + margin-left: -580px; + text-align: right; +} +nav ul { + list-style: none; + list-style-image: none; + font-size: 14px; + line-height: 24px; +} +nav ul li { + padding: 5px 0px; + line-height: 16px; +} +nav ul li.tag-h1 { + font-size: 1.2em; +} +nav ul li.tag-h1 a { + font-weight: bold; + color: #333; +} +nav ul li.tag-h2 + .tag-h1 { + margin-top: 10px; +} +nav ul a { + color: #666; +} +nav ul a:hover { + color: #999; } - - -/* Footer */ footer { - width:170px; - float:left; - position:fixed; - bottom:10px; - padding-left: 50px; + width: 180px; + position: fixed; + left: 50%; + margin-left: -530px; + bottom: 20px; + text-align: right; + line-height: 16px; } -@media print, screen and (max-width: 960px) { - +@media print, screen and (max-width: 1060px) { div.wrapper { - width:auto; - margin:0; + width: auto; + margin: 0; } - header, section, footer { - float:none; - position:static; - width:auto; + nav { + display: none; } - footer { - border-top: 1px solid #ccc; - margin:0 84px 0 50px; - padding:0; - } - - header { - padding-right:320px; + header, section, footer { + float: none; + } + header h1, section h1, footer h1 { + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; } - section { - padding:20px 84px 20px 50px; - margin:0 0 20px; + #banner { + width: 100%; + } + #banner .downloads { + margin-right: 60px; + } + #banner #logo { + margin-right: 15px; } - header a small { - display:inline; + section { + border: 1px solid #e5e5e5; + border-width: 1px 0; + padding: 20px auto; + margin: 190px auto 20px; + max-width: 600px; } - header ul { - position:absolute; - right:130px; - top:84px; + footer { + text-align: center; + margin: 20px auto; + position: relative; + left: auto; + bottom: auto; + width: auto; } } - @media print, screen and (max-width: 720px) { body { - word-wrap:break-word; + word-wrap: break-word; } header { - padding:10px 20px 0; - margin-right: 0; + padding: 20px 20px; + margin: 0; + } + header h1 { + font-size: 32px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; + } + header p { + display: none; } - section { - padding:10px 0 10px 20px; - margin:0 0 30px; + #banner { + top: 80px; + } + #banner .fork { + float: left; + display: inline-block; + margin-left: 0px; + position: fixed; + left: 20px; } - footer { - margin: 0 0 0 30px; - } + section { + margin-top: 130px; + margin-bottom: 0px; + width: auto; + } header ul, header p.view { - position:static; + position: static; } } - @media print, screen and (max-width: 480px) { - - header ul li.download { - display:none; + header { + position: relative; + padding: 5px 0px; + min-height: 0px; + } + header h1 { + font-size: 24px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; } - footer { - margin: 0 0 0 20px; - } + section { + margin-top: 5px; + } - footer a{ - display:block; - } + #banner { + display: none; + } + header ul { + display: none; + } } - @media print { body { - padding:0.4in; - font-size:12pt; - color:#444; + padding: 0.4in; + font-size: 12pt; + color: #444; } -} \ No newline at end of file +} +@media print, screen and (max-height: 680px) { + footer { + text-align: center; + margin: 20px auto; + position: relative; + left: auto; + bottom: auto; + width: auto; + } +} +@media print, screen and (max-height: 480px) { + nav { + display: none; + } + + footer { + text-align: center; + margin: 20px auto; + position: relative; + left: auto; + bottom: auto; + width: auto; + } +} From efe719c7a0773918b21b214da77f1a62df6157a8 Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Sun, 28 Jul 2013 00:59:09 -0700 Subject: [PATCH 02/16] Create gh-pages branch via GitHub --- images/body-bg.png | Bin 0 -> 8859 bytes images/highlight-bg.jpg | Bin 0 -> 34222 bytes images/hr.png | Bin 0 -> 1037 bytes images/octocat-icon.png | Bin 0 -> 1651 bytes images/tar-gz-icon.png | Bin 0 -> 1671 bytes images/zip-icon.png | Bin 0 -> 1661 bytes index.html | 76 ++++--- javascripts/main.js | 54 +---- params.json | 1 + stylesheets/print.css | 226 +++++++++++++++++++++ stylesheets/pygment_trac.css | 137 +++++++------ stylesheets/stylesheet.css | 371 +++++++++++++++++++++++++++++++++++ 12 files changed, 702 insertions(+), 163 deletions(-) create mode 100644 images/body-bg.png create mode 100644 images/highlight-bg.jpg create mode 100644 images/hr.png create mode 100644 images/octocat-icon.png create 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    Learn Data Science

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    Open content for self-directed learning in data science

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    LearnDataScience

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    Open content for self-directed learning in data science

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    Learn Data Science

    @@ -131,13 +123,15 @@

  • Z0. A quick tour of the IPython notebook.ipynb
  • Z1. Appendix 1 Plotting code snippets.ipynb
    • -

    - +

    + + + + +
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How to use this content.ipynb\r\n* A1. Linear Regression - Overview.ipynb\r\n* A2. Linear Regression - Data Exploration - Lending Club.ipynb\r\n* A3. Linear Regression - Analysis.ipynb\r\n* B1. Logistic Regression - Overview.ipynb\r\n* B1a. Odds, LogOdds and Logit Function .ipynb\r\n* B2. Logistic Regression - Data Exploration.ipynb\r\n* B3. Logistic Regression - Analysis.ipynb\r\n* C1. Random Forests - Overview.ipynb\r\n* C2. Random Forests - Data Exploration.ipynb\r\n* C3. Random Forests - Analysis.ipynb\r\n* D1. K-Means Clustering - Overview.ipynb\r\n* D2. K-Means Clustering - Data Exploration.ipynb\r\n* D3. K-Means Clustering Analysis.ipynb\r\n* WA1. Linear Regression Overview Worksheet.ipynb\r\n* WA2. Linear Regression - Data Exploration - Lending Club Worksheet.ipynb\r\n* WA3. Linear Regression - Analysis Worksheet.ipynb\r\n* WB3. Logistic Regression - Analysis- Worksheet.ipynb\r\n* WC3. Random Forests - Analysis - Worksheet.ipynb\r\n* WD2. 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+ border: solid 1px #ddd; + padding: 0 3px; +} + +pre { + padding: 20px; + background: #303030; + color: #f2f2f2; + text-shadow: none; + overflow: auto; +} +pre code { + color: #f2f2f2; + background-color: #303030; + border: none; + padding: 0; +} + +ul, ol, dl { + margin-bottom: 20px; +} + + +/* COMMON STYLES */ + +hr { + height: 1px; + line-height: 1px; + margin-top: 1em; + padding-bottom: 1em; + border: none; + background: transparent url('../images/hr.png') 50% 0 no-repeat; +} + +strong { + font-weight: bold; +} + +em { + font-style: italic; +} + +table { + width: 100%; + border: 1px solid #ebebeb; +} + +th { + font-weight: 500; +} + +td { + border: 1px solid #ebebeb; + text-align: center; + font-weight: 300; +} + +form { + background: #f2f2f2; + padding: 20px; + +} + + +/* GENERAL ELEMENT TYPE STYLES */ + +h1 { + font-size: 32px; +} + +h2 { + font-size: 22px; + font-weight: bold; + color: #303030; + margin-bottom: 8px; +} + +h3 { + color: #d5000d; + font-size: 18px; + font-weight: bold; + margin-bottom: 8px; +} + +h4 { + font-size: 16px; + color: #303030; + font-weight: bold; +} + +h5 { + font-size: 1em; + color: #303030; +} + +h6 { + font-size: .8em; + color: #303030; +} + +p { + font-weight: 300; + margin-bottom: 20px; +} + +a { + text-decoration: none; +} + +p a { + font-weight: 400; +} + +blockquote { + font-size: 1.6em; + border-left: 10px solid #e9e9e9; + margin-bottom: 20px; + padding: 0 0 0 30px; +} + +ul li { + list-style: disc inside; + padding-left: 20px; +} + +ol li { + list-style: decimal inside; + padding-left: 3px; +} + +dl dt { + color: #303030; +} + +footer { + background: transparent url('../images/hr.png') 0 0 no-repeat; + margin-top: 40px; + padding-top: 20px; + padding-bottom: 30px; + font-size: 13px; + color: #aaa; +} + +footer a { + color: #666; +} +footer a:hover { + color: #444; +} + +/* MISC */ +.clearfix:after { + clear: both; + content: '.'; + display: block; + visibility: hidden; + height: 0; +} + +.clearfix {display: inline-block;} +* html .clearfix {height: 1%;} +.clearfix {display: block;} + +/* #Media Queries +================================================== */ + +/* Smaller than standard 960 (devices and browsers) */ +@media only screen and (max-width: 959px) {} + +/* Tablet Portrait size to standard 960 (devices and browsers) */ +@media only screen and (min-width: 768px) and (max-width: 959px) {} + +/* All Mobile Sizes (devices and browser) */ +@media only screen and (max-width: 767px) { + header { + padding-top: 10px; + padding-bottom: 10px; + } + #downloads { + margin-bottom: 25px; + } + #download-zip, #download-tar-gz { + display: none; + } + .inner { + width: 94%; + margin: 0 auto; + } +} + +/* Mobile Landscape Size to Tablet Portrait (devices and browsers) */ +@media only screen and (min-width: 480px) and (max-width: 767px) {} + +/* Mobile Portrait Size to Mobile Landscape Size (devices and browsers) */ +@media only screen and (max-width: 479px) {} From 9c7637c5bfa7495d78419d3613abffd9a0955a5e Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Sun, 28 Jul 2013 01:00:30 -0700 Subject: [PATCH 03/16] Update README.md --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index 8354d60..51d5d85 100755 --- a/README.md +++ b/README.md @@ -1,6 +1,3 @@ -Learn Data Science -================== - Who --- From 6405838b1b5fbc8fc9040a2b78d9e5f307f185a8 Mon Sep 17 00:00:00 2001 From: nborwankar Date: Mon, 5 Aug 2013 08:43:14 -0700 Subject: [PATCH 04/16] added CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 0000000..9cef238 --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +learnds.com From b209adc5a8f38d9e70a105f0f3d86f29d3a711a9 Mon Sep 17 00:00:00 2001 From: nborwankar Date: Mon, 5 Aug 2013 09:16:15 -0700 Subject: [PATCH 05/16] README changed to include nbk index --- README.md | 57 ++++++++++++++++++++++++++++++------------------------- 1 file changed, 31 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index 51d5d85..55ac4a3 100755 --- a/README.md +++ b/README.md @@ -1,3 +1,4 @@ + Who --- @@ -51,7 +52,6 @@ Where Today github, tomorrow the world. - How --- @@ -59,28 +59,33 @@ Learn Data Science is based on content developed by me (Nitin Borwankar) for the Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. Extensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file. -IPython Notebooks at beta --------------------------- -* A0. How to use this content.ipynb -* A1. Linear Regression - Overview.ipynb -* A2. Linear Regression - Data Exploration - Lending Club.ipynb -* A3. Linear Regression - Analysis.ipynb -* B1. Logistic Regression - Overview.ipynb -* B1a. Odds, LogOdds and Logit Function .ipynb -* B2. Logistic Regression - Data Exploration.ipynb -* B3. Logistic Regression - Analysis.ipynb -* C1. Random Forests - Overview.ipynb -* C2. Random Forests - Data Exploration.ipynb -* C3. Random Forests - Analysis.ipynb -* D1. K-Means Clustering - Overview.ipynb -* D2. K-Means Clustering - Data Exploration.ipynb -* D3. K-Means Clustering Analysis.ipynb -* WA1. Linear Regression Overview Worksheet.ipynb -* WA2. Linear Regression - Data Exploration - Lending Club Worksheet.ipynb -* WA3. Linear Regression - Analysis Worksheet.ipynb -* WB3. Logistic Regression - Analysis- Worksheet.ipynb -* WC3. Random Forests - Analysis - Worksheet.ipynb -* WD2. K-Means Clustering - Data Exploration-Worksheet.ipynb -* WD3. K-Means Clustering Analysis - Worksheet.ipynb -* Z0. A quick tour of the IPython notebook.ipynb -* Z1. Appendix 1 Plotting code snippets.ipynb +IPython Notebooks at Beta. +-------------------------- + +* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20you%20begin.ipynb) +* [A0. How to use this content](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20How%20to%20use%20this%20content.ipynb) +* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb) +* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb) +* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb) +* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb) +* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb) +* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb) +* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb) +* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb) +* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb) +* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb) +* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb) +* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb) +* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb) +* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb) +* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb) +* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb) +* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb) +* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb) +* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb) +* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb) +* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb) +* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb) + + + From 5f9bcd49206354dcbee76ccee6f5dc5bb397b3dd Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Mon, 5 Aug 2013 09:21:20 -0700 Subject: [PATCH 06/16] Create gh-pages branch via GitHub --- images/bg_hr.png | Bin 0 -> 943 bytes images/blacktocat.png | Bin 0 -> 1428 bytes images/icon_download.png | Bin 0 -> 1162 bytes images/sprite_download.png | Bin 0 -> 16799 bytes index.html | 121 ++++---- params.json | 2 +- stylesheets/pygment_trac.css | 117 ++++---- stylesheets/stylesheet.css | 566 +++++++++++++++++++---------------- 8 files changed, 432 insertions(+), 374 deletions(-) create mode 100644 images/bg_hr.png create mode 100644 images/blacktocat.png create mode 100644 images/icon_download.png create mode 100644 images/sprite_download.png diff --git a/images/bg_hr.png b/images/bg_hr.png new file mode 100644 index 0000000000000000000000000000000000000000..7973bd69888c7e10ccad1111d555ceabb7cd99b6 GIT binary patch literal 943 zcmaJ=O^ee&7!FiK7FWCot{@Ck@nrMW&tx0B-6VAbrk1u~FTzffX&bu9#AIsIdef8t z!QZfdz=K}>3m(LO;6X3qN}Y6@>cJYA%)G<%Jn!ec>9im1@7>wsIBwrMF}iHO!q%;8 zSJ@xEd~(FL18NRvkBsOXMVM>4WQc*~qcQGc17IjxRnj!O_^B1gan0x#EWT48PK->5B2>mI;LIx zC*FSw$Nfc!g)WZCEOJ=mM)}lLsOk|$ltg_(&ax_YCWMlBLPDVT%D_gB7o_$YZ`-OB z#1sV%whRq21>W;qwN$N?OUGtQQe;JvOsQrna;+v+j8dth=*?orHHb6waX>S!yXCgT zo!oR3{E&GzaOAzfZYv@_Sf{LdyJInS>TS60&R9%yCs$y>2x(*gYIJtRrYAja$Ceq} z!N&oc_K1!3-Ft`U>`CM;quEbB4KG%!MovB*9_3!QzFhqHwrbwK|Doo-y>auDJNSP6 T=d)j*_4El@X4^PFK7I8YBT*xD literal 0 HcmV?d00001 diff --git a/images/blacktocat.png b/images/blacktocat.png new file mode 100644 index 0000000000000000000000000000000000000000..6e264fe57a2e35a2855405ac7d4102c3f6ddcdae GIT binary patch literal 1428 zcmeAS@N?(olHy`uVBq!ia0vp^av;pX1|+Qw)-3{3k|nMYCBgY=CFO}lsSJ)O`AMk? zp1FzXsX?iUDV2pMQ*9U+n3Xa^B1$5BeXNr6bM+EIYV;~{3xK*A7;Nk-3KEmEQ%e+* zQqwc@Y?a>c-mj#PnPRIHZt82`Ti~3Uk?B!Ylp0*+7m{3+ootz+WN)WnQ(*-(AUCxn zQK2F?C$HG5!d3}vt`(3C64qBz04piUwpD^SD#ABF!8yMuRl!uxKsVXI%s|1+P|wiV z#N6CmN5ROz&_Lh7NZ-&%*U;R`*vQJjKmiJrfVLH-q*(>IxIyg#@@$ndN=gc>^!3Zj z%k|2Q_413-^$jg8EkR}&8R-I5=oVMzl_XZ^<`pZ$OmImpPAEg{v+u2}(t{7puX=A(aKG z`a!A1`K3k4z=%sz23b{L-^Aq1JP;qO z-q+X4Gq1QLF)umQ)5TT^Xo6m5W{Q=eg`=5?o13Glvx}*rp{t>#shg3DvyriZv5}jZ ztD`wguSMv>2~2MaLa!4}y`ZF!TL84#CABECEH%ZgC_h&L>}9J=EN(GzcCm0X zaRr%YgxxI=y(w7S0@dq`Q?EYIG5Vm0MT%&c5HR(CnDAr^T6f1avxRvmvnsN+?-j}Z~1)Zr#rqzrt`edmo44*B<0=C4>mrxHF6$p zVws~UocMfeI`gB8pYMLYTzA87`NOI2w2B*JM5L`^AkN4AFQu&S+6ULTPjv;vzl4& z-eaK_F|D4~l3hzBSF~icNT@MID=v+_X`vpuvf=8+S(|^vlRdHe0<)v-^wiVR3w=TQ)uFA9F z>vmqc-mj#PnPRIHZt82`Ti~3Uk?B!Ylp0*+7m{3+ootz+WN)WnQ(*-(AUCxn zQK2F?C$HG5!d3}vt`(3C64qBz04piUwpD^SD#ABF!8yMuRl!uxKsVXI%s|1+P|wiV z#N6CmN5ROz&_Lh7NZ-&%*U;R`*vQJjKmiJrfVLH-q*(>IxIyg#@@$ndN=gc>^!3Zj z%k|2Q_413-^$jg8EkR}&8R-I5=oVMzl_XZ^<`pZ$OmImpPAEg{v+u2}(t{7puX=A(aKG z`a!A1`K3k4z=%sz23b{L-^Aq1JP;qO z-q+X4Gq1QLF)umQ)5TT^Xo6m5W{Q=$skw`#i#v$3O_v5UEZv#YC% zp@9obuSMv>2~2MaLa!N4y`ZF!TL84#CABECEH%ZgC_h&L>}9J=+-@<(X&zK> z3U0TU;MA)Rbc{YIVv!;mCIn19ASOK70y*%6pPC0u?M1+3t#h8?05D7Z^K@|xskoK& z=l_5E!ww8;ZH!Ed#V+%1n6Rkg{=V8A2QTsNE8^> zvHmCezoM^A29GnE>#ih4F*YzTGbm`! 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a/index.html b/index.html index 9ad63c6..dedbfbd 100644 --- a/index.html +++ b/index.html @@ -1,40 +1,37 @@ + - - - - - - - - Learn Data Science by nborwankar + + + + + + + Learn Data Science -
    -
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    Learn Data Science

    -

    Open content for self-directed learning in data science

    -
    - -
    - Download .zip - Download .tar.gz - View on GitHub -
    + +
    +
    + View on GitHub -
    +

    Learn Data Science

    +

    Open content for self-directed learning in data science

    -
    -

    -Learn Data Science

    +
    + Download this project as a .zip file + Download this project as a tar.gz file +
    +
    +
    -

    + +
    +
    +

    Who

      @@ -96,42 +93,46 @@

      Extensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.

      -IPython Notebooks at beta

      +IPython Notebooks at Beta.

    - - - + +
    - -
    + + + + + - \ No newline at end of file + diff --git a/params.json b/params.json index f57aa6a..ab45c11 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Learn Data Science\r\n==================\r\n\r\nWho\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at beta \r\n--------------------------\r\n* A0. How to use this content.ipynb\r\n* A1. Linear Regression - Overview.ipynb\r\n* A2. Linear Regression - Data Exploration - Lending Club.ipynb\r\n* A3. Linear Regression - Analysis.ipynb\r\n* B1. Logistic Regression - Overview.ipynb\r\n* B1a. Odds, LogOdds and Logit Function .ipynb\r\n* B2. Logistic Regression - Data Exploration.ipynb\r\n* B3. Logistic Regression - Analysis.ipynb\r\n* C1. Random Forests - Overview.ipynb\r\n* C2. Random Forests - Data Exploration.ipynb\r\n* C3. Random Forests - Analysis.ipynb\r\n* D1. K-Means Clustering - Overview.ipynb\r\n* D2. K-Means Clustering - Data Exploration.ipynb\r\n* D3. K-Means Clustering Analysis.ipynb\r\n* WA1. Linear Regression Overview Worksheet.ipynb\r\n* WA2. Linear Regression - Data Exploration - Lending Club Worksheet.ipynb\r\n* WA3. Linear Regression - Analysis Worksheet.ipynb\r\n* WB3. Logistic Regression - Analysis- Worksheet.ipynb\r\n* WC3. Random Forests - Analysis - Worksheet.ipynb\r\n* WD2. K-Means Clustering - Data Exploration-Worksheet.ipynb\r\n* WD3. K-Means Clustering Analysis - Worksheet.ipynb\r\n* Z0. A quick tour of the IPython notebook.ipynb\r\n* Z1. Appendix 1 Plotting code snippets.ipynb\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"\r\nWho\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. 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Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. 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Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)\r\n\r\n\r\n\r\n","google":"","note":"Don't delete this file! 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- padding-bottom: 10px; +@media screen and (max-width: 480px) { + body { + font-size:14px; } + #downloads { - margin-bottom: 25px; - } - #download-zip, #download-tar-gz { display: none; } + .inner { - width: 94%; - margin: 0 auto; + min-width: 320px; + max-width: 480px; + } + + #project_title { + font-size: 32px; + } + + h1 { + font-size: 28px; + } + + h2 { + font-size: 24px; + } + + h3 { + font-size: 21px; } -} -/* Mobile Landscape Size to Tablet Portrait (devices and browsers) */ -@media only screen and (min-width: 480px) and (max-width: 767px) {} + h4 { + font-size: 18px; + } + + h5 { + font-size: 14px; + } -/* Mobile Portrait Size to Mobile Landscape Size (devices and browsers) */ -@media only screen and (max-width: 479px) {} + h6 { + font-size: 12px; + } + + code, pre { + min-width: 320px; + max-width: 480px; + font-size: 11px; + } + +} From cf9d41419f0fcf7e2953d085ea3029fe51ab416b Mon Sep 17 00:00:00 2001 From: nborwankar Date: Mon, 5 Aug 2013 09:36:10 -0700 Subject: [PATCH 07/16] edit to notebook url --- ...to use this content.ipynb => A0. How to use this content.ipynb | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename notebooks/A0. How to use this content.ipynb => A0. How to use this content.ipynb (100%) diff --git a/notebooks/A0. How to use this content.ipynb b/A0. How to use this content.ipynb similarity index 100% rename from notebooks/A0. How to use this content.ipynb rename to A0. How to use this content.ipynb From 66bf70c44880834d8c6fe0d2ffb4057ba9c67e6b Mon Sep 17 00:00:00 2001 From: nborwankar Date: Mon, 5 Aug 2013 09:40:20 -0700 Subject: [PATCH 08/16] edit to notebook name and content --- notebooks/A0. Before you begin.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/A0. Before you begin.ipynb b/notebooks/A0. Before you begin.ipynb index a8adfe8..d27599c 100755 --- a/notebooks/A0. Before you begin.ipynb +++ b/notebooks/A0. Before you begin.ipynb @@ -1,6 +1,6 @@ { "metadata": { - "name": "A0. Before you begin" + "name": "A0. Before You Begin" }, "nbformat": 3, "nbformat_minor": 0, @@ -80,4 +80,4 @@ "metadata": {} } ] -} \ No newline at end of file +} From e301e60d8c61b77a1c10e7a6dec58361abdf5ee8 Mon Sep 17 00:00:00 2001 From: nborwankar Date: Mon, 5 Aug 2013 09:47:38 -0700 Subject: [PATCH 09/16] removed a notebook --- A0. How to use this content.ipynb | 74 ------------------------------- 1 file changed, 74 deletions(-) delete mode 100755 A0. How to use this content.ipynb diff --git a/A0. How to use this content.ipynb b/A0. How to use this content.ipynb deleted file mode 100755 index 05e833e..0000000 --- a/A0. How to use this content.ipynb +++ /dev/null @@ -1,74 +0,0 @@ -{ - "metadata": { - "name": "A0. How to use this content" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before you begin\n", - "================" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### About the content\n", - "\n", - "The content for OpenDST beta is written in IPython Notebook. This is a technology that allows executable code to be run via a browser with the results visible right in the browser. For this edition of the content we are using Python code for executable content. With IPython Notebook the executable code could be R or even MATLAB. \n", - "\n", - "\n", - "### IPython Notebook and Python\n", - "\n", - "* If you are new to IPython Notebook see http://ipython.org/notebook \n", - "* If you are new to Python see http://python.org\n", - "* If you are new to NumPy/SciPy/matplotlib/pandas see http://www.scipy.org/\n", - "\n", - "#### Important\n", - "\n", - "Please note this very important point before continuing.\n", - "When you execute code somewhere in the middle of a notebook, the code fragment may be dependent on imports, intermediate results in local variables and other such initializations in preceding cells. So it is better to execute code cells sequentially, starting at the top when new to IPython Notebook., to avoid being confused by such extraneous errors. \n", - "\n", - "Even so if you want to dive in and not have to execute each preceding code cell then you'll need to execute the menu item 'Cell->Run All'. This executes each code cell for you sequentially once, so from that point on all dependencies on prior code cells are satisfied.\n", - "While the code is running you'll see a flashing message towards the top right corner of the notebook, in the grey menubar, which says 'Kernel Busy'.\n", - "This means the code is being executed once through sequentially. Expand the browser to full screen to make sure you can see the message. When the message goes away and stays that way for 10 seconds or so, the execution is complete. You may now dive right in. \n", - "\n", - "If, during 'run all' you see a pink colored warning message, it can be ignored - it appears to be a Python or IPython issue that does not affect our content. To get rid of it for aesthetic reasons, click on the cell above it and hit shift-enter. It should disappear. Basically, executing the code that generated the message a second time makes the message disappear.\n", - "\n", - "#### Actually executing code and suchlike\n", - "\n", - "* To execute the code in a particular cell, click on the cell and hit shift-enter.\n", - "* Before you execute the code in an arbitrary cell it is good to run all the code once so that all imports and variables are initialised.\n", - "* To execute all code in a notebook click Menu->Cell->RunAll. You should do this at the start of reading each new notebook.\n", - "* If you see a pink colored warning message, it can be ignored. To get rid of it for aesthetic reasons, click on the cell above it and hit shift enter. It should disappear. \n", - "* On the other hand if you get a full blown exception, it means something went wrong. Typically it means you didn't run Menu->Cell->RunAll and something did not get initialised. The error message itself should tell you more.\n", - "\n", - "* You will see code cells that say \"TRY THIS\" - do actually try what it's asking you to. The notebook is meant to be interactive.\n", - "* The content gets progressively more challenging and the intent is that a community support system will develop around the content. In the meanwhile there's StackOverflow.\n", - "\n", - "* Make sure you have all the supporting images and datasets or you'll see lots of exceptions.\n", - "* See installation instructions at http://opendst.com if you want to create your own installation.\n", - "* If you're having problems that are hard to fix, a fresh installation, from instructions at opendst.com, is recommended.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file From b95c02cff28457f328f451fffa9fcdf5277da619 Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Mon, 5 Aug 2013 09:48:40 -0700 Subject: [PATCH 10/16] Create gh-pages branch via GitHub --- index.html | 3 +-- params.json | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/index.html b/index.html index dedbfbd..75d66ae 100644 --- a/index.html +++ b/index.html @@ -96,8 +96,7 @@

    IPython Notebooks at Beta.

      -
    • A0. Before you begin
      • -
    • A0. How to use this content
      • +
    • A0. Before you begin
    • A1. Linear Regression - Overview
    • A2. Linear Regression - Data Exploration - Lending Club
    • A3. Linear Regression - Analysis
      • diff --git a/params.json b/params.json index ab45c11..ccc3328 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"\r\nWho\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20you%20begin.ipynb)\r\n* [A0. How to use this content](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20How%20to%20use%20this%20content.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)\r\n\r\n\r\n\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"\r\nWho\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)\r\n\r\n\r\n\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From a9bd384b09645944ce79cf1e5daf68c2bdff6afb Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Mon, 5 Aug 2013 10:02:35 -0700 Subject: [PATCH 11/16] Create gh-pages branch via GitHub --- index.html | 69 ++--- params.json | 2 +- stylesheets/pygment_trac.css | 117 ++++---- stylesheets/stylesheet.css | 566 ++++++++++++++++------------------- 4 files changed, 347 insertions(+), 407 deletions(-) diff --git a/index.html b/index.html index 75d66ae..915a7ce 100644 --- a/index.html +++ b/index.html @@ -1,37 +1,37 @@ - - - - - - - - Learn Data Science + + + + + + + + Learn Data Science by nborwankar +
      +
      - -
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      - View on GitHub +
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      Learn Data Science

      +

      Open content for self-directed learning in data science

      +
      -

      Learn Data Science

      -

      Open content for self-directed learning in data science

      +
      + Download .zip + Download .tar.gz + View on GitHub +
      -
      - Download this project as a .zip file - Download this project as a tar.gz file -
      -
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      - -
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      +
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      Who

      -
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      + - - - - + + +
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      - + \ No newline at end of file diff --git a/params.json b/params.json index ccc3328..01bf9d7 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"\r\nWho\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)\r\n\r\n\r\n\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WD2. 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- -/******************************************************************************* -MeyerWeb Reset -*******************************************************************************/ - +/* http://meyerweb.com/eric/tools/css/reset/ + v2.0 | 20110126 + License: none (public domain) +*/ html, body, div, span, applet, object, iframe, h1, h2, h3, h4, h5, h6, p, blockquote, pre, a, abbr, acronym, address, big, cite, code, @@ -18,410 +11,361 @@ b, u, i, center, dl, dt, dd, ol, ul, li, fieldset, form, label, legend, table, caption, tbody, tfoot, thead, tr, th, td, -article, aside, canvas, details, embed, -figure, figcaption, footer, header, hgroup, +article, aside, canvas, details, embed, +figure, figcaption, footer, header, hgroup, menu, nav, output, ruby, section, summary, time, mark, audio, video { - margin: 0; - padding: 0; - border: 0; - font: inherit; - vertical-align: baseline; + margin: 0; + padding: 0; + border: 0; + font-size: 100%; + font: inherit; + vertical-align: baseline; } - /* HTML5 display-role reset for older browsers */ -article, aside, details, figcaption, figure, +article, aside, details, figcaption, figure, footer, header, hgroup, menu, nav, section { - display: block; + display: block; +} +body { + line-height: 1; } - ol, ul { - list-style: none; + list-style: none; } - blockquote, q { + quotes: none; +} +blockquote:before, blockquote:after, +q:before, q:after { + content: ''; + content: none; } - table { - border-collapse: collapse; - border-spacing: 0; + border-collapse: collapse; + border-spacing: 0; } -/******************************************************************************* -Theme Styles -*******************************************************************************/ - +/* LAYOUT STYLES */ body { - box-sizing: border-box; - color:#373737; - background: #212121; - font-size: 16px; - font-family: 'Myriad Pro', Calibri, Helvetica, Arial, sans-serif; - line-height: 1.5; - -webkit-font-smoothing: antialiased; + font-size: 1em; + line-height: 1.5; + background: #e7e7e7 url(../images/body-bg.png) 0 0 repeat; + font-family: 'Helvetica Neue', Helvetica, Arial, serif; + text-shadow: 0 1px 0 rgba(255, 255, 255, 0.8); + color: #6d6d6d; } -h1, h2, h3, h4, h5, h6 { - margin: 10px 0; - font-weight: 700; - color:#222222; - font-family: 'Lucida Grande', 'Calibri', Helvetica, Arial, sans-serif; - letter-spacing: -1px; +a { + color: #d5000d; } - -h1 { - font-size: 36px; - font-weight: 700; +a:hover { + color: #c5000c; } -h2 { - padding-bottom: 10px; - font-size: 32px; - background: url('../images/bg_hr.png') repeat-x bottom; +header { + padding-top: 35px; + padding-bottom: 25px; } -h3 { - font-size: 24px; -} - -h4 { - font-size: 21px; -} - -h5 { - font-size: 18px; +header h1 { + font-family: 'Chivo', 'Helvetica Neue', Helvetica, Arial, serif; font-weight: 900; + letter-spacing: -1px; + font-size: 48px; + color: #303030; + line-height: 1.2; } -h6 { - font-size: 16px; +header h2 { + letter-spacing: -1px; + font-size: 24px; + color: #aaa; + font-weight: normal; + line-height: 1.3; } -p { - margin: 10px 0 15px 0; +#container { + background: transparent url(../images/highlight-bg.jpg) 50% 0 no-repeat; + min-height: 595px; } -footer p { - color: #f2f2f2; +.inner { + width: 620px; + margin: 0 auto; } -a { - text-decoration: none; - color: #007edf; - text-shadow: none; - - transition: color 0.5s ease; - transition: text-shadow 0.5s ease; - -webkit-transition: color 0.5s ease; - -webkit-transition: text-shadow 0.5s ease; - -moz-transition: color 0.5s ease; - -moz-transition: text-shadow 0.5s ease; - -o-transition: color 0.5s ease; - -o-transition: text-shadow 0.5s ease; - -ms-transition: color 0.5s ease; - -ms-transition: text-shadow 0.5s ease; +#container .inner img { + max-width: 100%; } -#main_content a:hover { - color: #0069ba; - text-shadow: #0090ff 0px 0px 2px; +#downloads { + margin-bottom: 40px; +} + +a.button { + -moz-border-radius: 30px; + -webkit-border-radius: 30px; + border-radius: 30px; + border-top: solid 1px #cbcbcb; + border-left: solid 1px #b7b7b7; + border-right: solid 1px #b7b7b7; + border-bottom: solid 1px #b3b3b3; + color: #303030; + line-height: 25px; + font-weight: bold; + font-size: 15px; + padding: 12px 8px 12px 8px; + display: block; + float: left; + width: 179px; + margin-right: 14px; + background: #fdfdfd; /* Old browsers */ + background: -moz-linear-gradient(top, #fdfdfd 0%, #f2f2f2 100%); /* FF3.6+ */ + background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#fdfdfd), color-stop(100%,#f2f2f2)); /* Chrome,Safari4+ */ + background: -webkit-linear-gradient(top, #fdfdfd 0%,#f2f2f2 100%); /* Chrome10+,Safari5.1+ */ + background: -o-linear-gradient(top, #fdfdfd 0%,#f2f2f2 100%); /* Opera 11.10+ */ + background: -ms-linear-gradient(top, #fdfdfd 0%,#f2f2f2 100%); /* IE10+ */ + background: linear-gradient(top, #fdfdfd 0%,#f2f2f2 100%); /* W3C */ + filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#fdfdfd', endColorstr='#f2f2f2',GradientType=0 ); /* IE6-9 */ + -webkit-box-shadow: 10px 10px 5px #888; + -moz-box-shadow: 10px 10px 5px #888; + box-shadow: 0px 1px 5px #e8e8e8; +} +a.button:hover { + border-top: solid 1px #b7b7b7; + border-left: solid 1px #b3b3b3; + border-right: solid 1px #b3b3b3; + border-bottom: solid 1px #b3b3b3; + background: #fafafa; /* Old browsers */ + background: -moz-linear-gradient(top, #fdfdfd 0%, #f6f6f6 100%); /* FF3.6+ */ + background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#fdfdfd), color-stop(100%,#f6f6f6)); /* Chrome,Safari4+ */ + background: -webkit-linear-gradient(top, #fdfdfd 0%,#f6f6f6 100%); /* Chrome10+,Safari5.1+ */ + background: -o-linear-gradient(top, #fdfdfd 0%,#f6f6f6 100%); /* Opera 11.10+ */ + background: -ms-linear-gradient(top, #fdfdfd 0%,#f6f6f6 100%); /* IE10+ */ + background: linear-gradient(top, #fdfdfd 0%,#f6f6f6, 100%); /* W3C */ + filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#fdfdfd', endColorstr='#f6f6f6',GradientType=0 ); /* IE6-9 */ +} + +a.button span { + padding-left: 50px; + display: block; + height: 23px; } -footer a:hover { - color: #43adff; - text-shadow: #0090ff 0px 0px 2px; +#download-zip span { + background: transparent url(../images/zip-icon.png) 12px 50% no-repeat; } - -em { - font-style: italic; +#download-tar-gz span { + background: transparent url(../images/tar-gz-icon.png) 12px 50% no-repeat; } - -strong { - font-weight: bold; +#view-on-github span { + background: transparent url(../images/octocat-icon.png) 12px 50% no-repeat; } - -img { - position: relative; - margin: 0 auto; - max-width: 739px; - padding: 5px; - margin: 10px 0 10px 0; - border: 1px solid #ebebeb; - - box-shadow: 0 0 5px #ebebeb; - -webkit-box-shadow: 0 0 5px #ebebeb; - -moz-box-shadow: 0 0 5px #ebebeb; - -o-box-shadow: 0 0 5px #ebebeb; - -ms-box-shadow: 0 0 5px #ebebeb; +#view-on-github { + margin-right: 0; } -pre, code { - width: 100%; +code, pre { + font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal; color: #222; - background-color: #fff; - - font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal, monospace; + margin-bottom: 30px; font-size: 14px; +} - border-radius: 2px; - -moz-border-radius: 2px; - -webkit-border-radius: 2px; - - - +code { + background-color: #f2f2f2; + border: solid 1px #ddd; + padding: 0 3px; } pre { - width: 100%; - padding: 10px; - box-shadow: 0 0 10px rgba(0,0,0,.1); + padding: 20px; + background: #303030; + color: #f2f2f2; + text-shadow: none; overflow: auto; } - -code { - padding: 3px; - margin: 0 3px; - box-shadow: 0 0 10px rgba(0,0,0,.1); -} - pre code { - display: block; - box-shadow: none; + color: #f2f2f2; + background-color: #303030; + border: none; + padding: 0; } -blockquote { - color: #666; +ul, ol, dl { margin-bottom: 20px; - padding: 0 0 0 20px; - border-left: 3px solid #bbb; } -ul, ol, dl { - margin-bottom: 15px -} -ul li { - list-style: inside; - padding-left: 20px; -} +/* COMMON STYLES */ -ol li { - list-style: decimal inside; - padding-left: 20px; +hr { + height: 1px; + line-height: 1px; + margin-top: 1em; + padding-bottom: 1em; + border: none; + background: transparent url('../images/hr.png') 50% 0 no-repeat; } -dl dt { +strong { font-weight: bold; } -dl dd { - padding-left: 20px; - font-style: italic; -} - -dl p { - padding-left: 20px; +em { font-style: italic; } -hr { - height: 1px; - margin-bottom: 5px; - border: none; - background: url('../images/bg_hr.png') repeat-x center; -} - table { - border: 1px solid #373737; - margin-bottom: 20px; - text-align: left; - } + width: 100%; + border: 1px solid #ebebeb; +} th { - font-family: 'Lucida Grande', 'Helvetica Neue', Helvetica, Arial, sans-serif; - padding: 10px; - background: #373737; - color: #fff; - } + font-weight: 500; +} td { - padding: 10px; - border: 1px solid #373737; - } + border: 1px solid #ebebeb; + text-align: center; + font-weight: 300; +} form { background: #f2f2f2; padding: 20px; + } -img { - width: 100%; - max-width: 100%; -} -/******************************************************************************* -Full-Width Styles -*******************************************************************************/ +/* GENERAL ELEMENT TYPE STYLES */ -.outer { - width: 100%; -} +h1 { + font-size: 32px; +} -.inner { - position: relative; - max-width: 640px; - padding: 20px 10px; - margin: 0 auto; -} +h2 { + font-size: 22px; + font-weight: bold; + color: #303030; + margin-bottom: 8px; +} -#forkme_banner { - display: block; - position: absolute; - top:0; - right: 10px; - z-index: 10; - padding: 10px 50px 10px 10px; - color: #fff; - background: url('../images/blacktocat.png') #0090ff no-repeat 95% 50%; - font-weight: 700; - box-shadow: 0 0 10px rgba(0,0,0,.5); - border-bottom-left-radius: 2px; - border-bottom-right-radius: 2px; -} - -#header_wrap { - background: #212121; - background: -moz-linear-gradient(top, #373737, #212121); - background: -webkit-linear-gradient(top, #373737, #212121); - background: -ms-linear-gradient(top, #373737, #212121); - background: -o-linear-gradient(top, #373737, #212121); - background: linear-gradient(top, #373737, #212121); -} - -#header_wrap .inner { - padding: 50px 10px 30px 10px; -} - -#project_title { - margin: 0; - color: #fff; - font-size: 42px; - font-weight: 700; - text-shadow: #111 0px 0px 10px; -} - -#project_tagline { - color: #fff; - font-size: 24px; +h3 { + color: #d5000d; + font-size: 18px; + font-weight: bold; + margin-bottom: 8px; +} + +h4 { + font-size: 16px; + color: #303030; + font-weight: bold; +} + +h5 { + font-size: 1em; + color: #303030; +} + +h6 { + font-size: .8em; + color: #303030; +} + +p { font-weight: 300; - background: none; - text-shadow: #111 0px 0px 10px; + margin-bottom: 20px; +} + +a { + text-decoration: none; } -#downloads { - position: absolute; - width: 210px; - z-index: 10; - bottom: -40px; - right: 0; - height: 70px; - background: url('../images/icon_download.png') no-repeat 0% 90%; +p a { + font-weight: 400; } -.zip_download_link { - display: block; - float: right; - width: 90px; - height:70px; - text-indent: -5000px; - overflow: hidden; - background: url(../images/sprite_download.png) no-repeat bottom left; +blockquote { + font-size: 1.6em; + border-left: 10px solid #e9e9e9; + margin-bottom: 20px; + padding: 0 0 0 30px; } -.tar_download_link { - display: block; - float: right; - width: 90px; - height:70px; - text-indent: -5000px; - overflow: hidden; - background: url(../images/sprite_download.png) no-repeat bottom right; - margin-left: 10px; +ul li { + list-style: disc inside; + padding-left: 20px; } -.zip_download_link:hover { - background: url(../images/sprite_download.png) no-repeat top left; +ol li { + list-style: decimal inside; + padding-left: 3px; } -.tar_download_link:hover { - background: url(../images/sprite_download.png) no-repeat top right; +dl dt { + color: #303030; } -#main_content_wrap { - background: #f2f2f2; - border-top: 1px solid #111; - border-bottom: 1px solid #111; +footer { + background: transparent url('../images/hr.png') 0 0 no-repeat; + margin-top: 40px; + padding-top: 20px; + padding-bottom: 30px; + font-size: 13px; + color: #aaa; } -#main_content { - padding-top: 40px; +footer a { + color: #666; +} +footer a:hover { + color: #444; } -#footer_wrap { - background: #212121; +/* MISC */ +.clearfix:after { + clear: both; + content: '.'; + display: block; + visibility: hidden; + height: 0; } +.clearfix {display: inline-block;} +* html .clearfix {height: 1%;} +.clearfix {display: block;} +/* #Media Queries +================================================== */ -/******************************************************************************* -Small Device Styles -*******************************************************************************/ +/* Smaller than standard 960 (devices and browsers) */ +@media only screen and (max-width: 959px) {} -@media screen and (max-width: 480px) { - body { - font-size:14px; - } +/* Tablet Portrait size to standard 960 (devices and browsers) */ +@media only screen and (min-width: 768px) and (max-width: 959px) {} +/* All Mobile Sizes (devices and browser) */ +@media only screen and (max-width: 767px) { + header { + padding-top: 10px; + padding-bottom: 10px; + } #downloads { + margin-bottom: 25px; + } + #download-zip, #download-tar-gz { display: none; } - .inner { - min-width: 320px; - max-width: 480px; - } - - #project_title { - font-size: 32px; - } - - h1 { - font-size: 28px; - } - - h2 { - font-size: 24px; - } - - h3 { - font-size: 21px; - } - - h4 { - font-size: 18px; - } - - h5 { - font-size: 14px; + width: 94%; + margin: 0 auto; } +} - h6 { - font-size: 12px; - } +/* Mobile Landscape Size to Tablet Portrait (devices and browsers) */ +@media only screen and (min-width: 480px) and (max-width: 767px) {} - code, pre { - min-width: 320px; - max-width: 480px; - font-size: 11px; - } - -} +/* Mobile Portrait Size to Mobile Landscape Size (devices and browsers) */ +@media only screen and (max-width: 479px) {} From 0d2824307e99068ed6ee44a207dee07433b9ebeb Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Tue, 3 Sep 2013 22:54:19 -0700 Subject: [PATCH 12/16] Create gh-pages branch via GitHub --- index.html | 10 ++++++---- params.json | 2 +- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/index.html b/index.html index 915a7ce..2d08c42 100644 --- a/index.html +++ b/index.html @@ -88,8 +88,8 @@

      How

      -

      Learn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org -Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. +

      Learn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org, which was sponsored by Pivotal Labs and Alpine Data Labs. +Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. Extensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.

      @@ -113,8 +113,10 @@

    • WA1. Linear Regression Overview Worksheet
    • WA2. Linear Regression - Data Exploration - Lending Club Worksheet
    • WA3. Linear Regression - Analysis Worksheet
      • -
    • WB3. Logistic Regression - Analysis- Worksheet
      • -
    • WC3. Random Forests - Analysis - Worksheet
      • +
    • WA4. Linear Regression - Data Cleanup Worksheet
      • +
    • WB3. Logistic Regression - Analysis Worksheet
      • +
    • WC3. Random Forests - Analysis Worksheet
      • +
    • WC4. Random Forests - Data Cleanup Worksheet
    • WD2. K-Means Clustering - Data Exploration-Worksheet
    • WD3. K-Means Clustering Analysis - Worksheet
    • Z0. A quick tour of the IPython notebook
      • diff --git a/params.json b/params.json index 01bf9d7..c686b7d 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)\r\n\r\n\r\n\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org, which was sponsored by Pivotal Labs and Alpine Data Labs.\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. \r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 4e8b02488eba90156eafc33ee1bcd068b0ebd488 Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Fri, 6 Sep 2013 11:32:57 -0700 Subject: [PATCH 13/16] Create gh-pages branch via GitHub --- index.html | 3 ++- params.json | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index 2d08c42..7a8e474 100644 --- a/index.html +++ b/index.html @@ -35,7 +35,8 @@

      Who

      What

      diff --git a/params.json b/params.json index c686b7d..4cccf15 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org, which was sponsored by Pivotal Labs and Alpine Data Labs.\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. \r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer \r\n(Sponsored by Pivotal Labs and Alpine Data Labs).\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org, which was sponsored by Pivotal Labs and Alpine Data Labs.\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. \r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From da1042497c7e25ddf51fe91be88016e2de438729 Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Sat, 7 Sep 2013 22:46:36 -0700 Subject: [PATCH 14/16] Create gh-pages branch via GitHub --- index.html | 10 +++++----- params.json | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/index.html b/index.html index 7a8e474..0aefcf3 100644 --- a/index.html +++ b/index.html @@ -89,15 +89,15 @@

      How

      -

      Learn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org, which was sponsored by Pivotal Labs and Alpine Data Labs. -Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. +

      Learn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org +Most of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. Extensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.

      IPython Notebooks at Beta.

        -
      • A0. Before you begin
        • +
      • A0. Before You Begin
      • A1. Linear Regression - Overview
      • A2. Linear Regression - Data Exploration - Lending Club
      • A3. Linear Regression - Analysis
        • @@ -115,8 +115,8 @@

      • WA2. Linear Regression - Data Exploration - Lending Club Worksheet
      • WA3. Linear Regression - Analysis Worksheet
      • WA4. Linear Regression - Data Cleanup Worksheet
        • -
      • WB3. Logistic Regression - Analysis Worksheet
        • -
      • WC3. Random Forests - Analysis Worksheet
        • +
      • WB3. Logistic Regression - Analysis- Worksheet
        • +
      • WC3. Random Forests - Analysis - Worksheet
      • WC4. Random Forests - Data Cleanup Worksheet
      • WD2. K-Means Clustering - Data Exploration-Worksheet
      • WD3. K-Means Clustering Analysis - Worksheet
        • diff --git a/params.json b/params.json index 4cccf15..8ed2129 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer \r\n(Sponsored by Pivotal Labs and Alpine Data Labs).\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org, which was sponsored by Pivotal Labs and Alpine Data Labs.\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available. \r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before you begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer \r\n(Sponsored by Pivotal Labs and Alpine Data Labs).\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before You Begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From be1134ad7a030cf032ad56bae38219dee3582590 Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Tue, 10 Sep 2013 16:30:37 -0700 Subject: [PATCH 15/16] Create gh-pages branch via GitHub --- index.html | 41 +++++++++++++++++++++++++++++++++++++++++ params.json | 2 +- 2 files changed, 42 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index 0aefcf3..fb07a18 100644 --- a/index.html +++ b/index.html @@ -122,6 +122,47 @@

      • WD3. K-Means Clustering Analysis - Worksheet
      • Z0. A quick tour of the IPython notebook
      • Z1. Appendix 1 Plotting code snippets
        • +

      +Background

      + +

      If you are unfamiliar with IPython Notebook you can start with http://ipython.org/notebook

      + +

      +Installation

      + +
        +
      • +

        Prerequisites
        +One of the following distributions is needed. Please note that even if you have Python installed it is important to have one of these distributions installed and the binary for this installation in your path. This is because these distributions come packaged with all the supplementary libraries needed and these have been historically difficult to install separately.

        + +
          +
        • EPD Free Enthought Python Distribution from http://enthought.com +
          • +
        • Anaconda Python from http://continuum.io +
          • +
        • Development has been done on v 1.5 of Anaconda distribution but EPD Free should work just as well.
          • +
        +
        • +

      • The following steps assume you have installed one of the distributions mentioned in prerequisites.

        • +
      • +

        From a zip or tar file

        + +
          +
        • download the zip or tar file
          • +
        • unpack the file to a directory called learnds
          • +
        • cd to the 'notebooks' subdirectory
          • +
        • start IPython Notebook 'ipython notebook --pylab=inline'
          • +
        +
        • +
      • +

        From the git repo

        + +
          +
        • clone the repo
          • +
        • cd to 'notebooks'
          • +
        • start IPython Notebook 'ipython notebook --pylab=inline'
          • +
        +
      diff --git a/params.json b/params.json index 8ed2129..37a6488 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer \r\n(Sponsored by Pivotal Labs and Alpine Data Labs).\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before You Begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"Learn Data Science","tagline":"Open content for self-directed learning in data science","body":"Who\r\n---\r\n\r\n* Nitin Borwankar http://twitter.com/nitin - primary developer \r\n(Sponsored by Pivotal Labs and Alpine Data Labs).\r\n\r\n\r\nWhat\r\n----\r\n\r\n* A collection of Data Science Learning materials in the form of IPython Notebooks.\r\n* Associated data sets.\r\n\r\nThe initial beta release consists of four major topics\r\n\r\n* Linear Regression\r\n* Logistic Regression\r\n* Random Forests\r\n* K-Means Clustering\r\n\r\nEach of the above has at least three IPython Notebooks covering\r\n\r\n* Overview (an exposition of the technique for the math-wary)\r\n* Data Exploration (the nuts and bolts of real world data wrangling)\r\n* Analysis (using the technique to get results)\r\n\r\nOne or more of these may have supplementary material.\r\nEach of these have worksheets that contain mostly the code sections so you can iteratively explore the code.\r\n\r\nThree openly available data sets are used. \r\n\r\n* For the Linear and Logistic Regression we use a data set on loans and interest rates provided by Learning Club http://learningclub.com \r\n* For Random Forests we use a data set of Android accelerometer and gyroscope readings used to predict body position and motion from the Human Activity Recognition project\r\nhttp://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones\r\n* UN data on economic indicators of countries\r\n\r\nWhy\r\n---\r\n\r\nThere's a need for open content to raise the level of awareness and training in basics, in the Data Science\r\nfield (circa early 2013).\r\n\r\nIPython Notebook provides an appropriate platform for rapid iterative exploration and learning.\r\n\r\nWhen\r\n----\r\n\r\nStarting in 2013 and intended to extend for a long while.\r\n\r\nWhere\r\n-----\r\n\r\nToday github, tomorrow the world. \r\n\r\nHow\r\n---\r\n\r\nLearn Data Science is based on content developed by me (Nitin Borwankar) for the Open Data Science Training project http://opendst.org\r\nMost of the content (circa July 2013) is copyright (c) Alpine Data Labs as per the license at opendst.org, and is freely available.\r\nExtensions to the content embodied in this projects content are also released under the same license - see the LICENSE.txt file.\r\n\r\nIPython Notebooks at Beta. \r\n-------------------------- \r\n\r\n* [A0. Before You Begin](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A0.%20Before%20You%20Begin.ipynb)\r\n* [A1. Linear Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A1.%20Linear%20Regression%20-%20Overview.ipynb)\r\n* [A2. Linear Regression - Data Exploration - Lending Club](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club.ipynb)\r\n* [A3. Linear Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/A3.%20Linear%20Regression%20-%20Analysis.ipynb)\r\n* [B1. Logistic Regression - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1.%20Logistic%20Regression%20-%20Overview.ipynb)\r\n* [B1a. Odds, LogOdds and Logit Function ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B1a.%20Odds%2C%20LogOdds%20and%20Logit%20Function%20.ipynb)\r\n* [B2. Logistic Regression - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B2.%20Logistic%20Regression%20-%20Data%20Exploration.ipynb)\r\n* [B3. Logistic Regression - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/B3.%20Logistic%20Regression%20-%20Analysis.ipynb)\r\n* [C1. Random Forests - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C1.%20Random%20Forests%20-%20Overview.ipynb)\r\n* [C2. Random Forests - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C2.%20Random%20Forests%20-%20Data%20Exploration.ipynb)\r\n* [C3. Random Forests - Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/C3.%20Random%20Forests%20-%20Analysis.ipynb)\r\n* [D1. K-Means Clustering - Overview](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D1.%20K-Means%20Clustering%20-%20Overview.ipynb)\r\n* [D2. K-Means Clustering - Data Exploration](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D2.%20K-Means%20Clustering%20-%20Data%20Exploration.ipynb)\r\n* [D3. K-Means Clustering Analysis](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)\r\n* [WA1. Linear Regression Overview Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA1.%20Linear%20Regression%20Overview%20Worksheet.ipynb)\r\n* [WA2. Linear Regression - Data Exploration - Lending Club Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA2.%20Linear%20Regression%20-%20Data%20Exploration%20-%20Lending%20Club%20Worksheet.ipynb)\r\n* [WA3. Linear Regression - Analysis Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA3.%20Linear%20Regression%20-%20Analysis%20Worksheet.ipynb)\r\n* [WA4. Linear Regression - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WA4.%20Linear%20Regression%20-%20Data%20Cleanup.ipynb)\r\n* [WB3. Logistic Regression - Analysis- Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WB3.%20Logistic%20Regression%20-%20Analysis-%20Worksheet.ipynb)\r\n* [WC3. Random Forests - Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC3.%20Random%20Forests%20-%20Analysis%20-%20Worksheet.ipynb)\r\n* [WC4. Random Forests - Data Cleanup Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WC4.%20Random%20Forests%20-%20Data%20Cleanup.ipynb)\r\n* [WD2. K-Means Clustering - Data Exploration-Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD2.%20K-Means%20Clustering%20-%20Data%20Exploration-Worksheet.ipynb)\r\n* [WD3. K-Means Clustering Analysis - Worksheet](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/WD3.%20K-Means%20Clustering%20Analysis%20-%20Worksheet.ipynb)\r\n* [Z0. A quick tour of the IPython notebook](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z0.%20A%20quick%20tour%20of%20the%20IPython%20notebook.ipynb)\r\n* [Z1. Appendix 1 Plotting code snippets ](http://nbviewer.ipython.org/urls/raw.github.com/nborwankar/LearnDataScience/master/notebooks/Z1.%20Appendix%201%20Plotting%20code%20snippets%20.ipynb)\r\n\r\n\r\nBackground \r\n----------\r\n\r\nIf you are unfamiliar with IPython Notebook you can start with http://ipython.org/notebook\r\n\r\n\r\nInstallation \r\n------------\r\n\r\n* Prerequisites \r\nOne of the following distributions is needed. Please note that even if you have Python installed it is important to have one of these distributions installed and the binary for this installation in your path. This is because these distributions come packaged with all the supplementary libraries needed and these have been historically difficult to install separately.\r\n\r\n * EPD Free Enthought Python Distribution from http://enthought.com\r\n * Anaconda Python from http://continuum.io\r\n * Development has been done on v 1.5 of Anaconda distribution but EPD Free should work just as well.\r\n\r\n* The following steps assume you have installed one of the distributions mentioned in prerequisites.\r\n\r\n* From a zip or tar file\r\n * download the zip or tar file \r\n * unpack the file to a directory called learnds\r\n * cd to the 'notebooks' subdirectory\r\n * start IPython Notebook 'ipython notebook --pylab=inline'\r\n \r\n* From the git repo\r\n * clone the repo\r\n * cd to 'notebooks'\r\n * start IPython Notebook 'ipython notebook --pylab=inline'\r\n","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From c0164b33fa7e9e54f115bd795beed55bfccfcc4d Mon Sep 17 00:00:00 2001 From: Nitin Borwankar Date: Mon, 6 Oct 2014 20:54:02 -0700 Subject: [PATCH 16/16] Update index.html fixed typo --- index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index fb07a18..4d8f178 100644 --- a/index.html +++ b/index.html @@ -36,7 +36,7 @@

      • Nitin Borwankar http://twitter.com/nitin - primary developer
        -(Sponsored by Pivotal Labs and Alpine Data Labs).
        • +(Sponsored by Pivotal Inc. and Alpine Data Labs).

      What

      @@ -175,4 +175,4 @@

    - \ No newline at end of file +