Allocation of units for batch7

[cimendes]

This is a work in progress! Template: #330

Description

The goal of this issue is to assess interest and have a pre-allocation of batch7's
teaching work and QA.

The units, overall work needed and release and delivery dates are listed
below.

Units

Admissions

• Unit
  • Adjustment to the new Python and Pandas versions.
  • Learning notebooks - minimum change: review
  • Example notebooks - minimum change: review
  • Exercise notebook - minimum change: review, new datasets
• To be released on 23 October 2023
• To be ready on: soon

SLU	Name	Last year instructor	Batch 7 instructor	Last year QA
SLU01	Pandas 101	@majkah0	@majkah0	@Jujulian3
SLU02	Subsetting Data in Pandas	@jgomes959	@jgomes959	@jgerebelo
SLU03	Visualization with Pandas & Matplotlib	@Gustavo-SF	@kagglekim	@SaraOGomes
Test		@fabiocruz		@danizao @majkah0 @minhhoang1023 @Gustavo-SF
Test on 23 October 2023

• Batch 7 QA Lead SLU01/SLU02/SLU03: @FilipaPereira78
• Batch 7 backup QA SLU01/SLU02/SLU03:@CaitlinHulse

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)
Both QA people do the test verification

Specialization 1 + Bootcamp

• Project manager: José Rebelo @jgerebelo
• Unit: minimum changes based on issues from last year and this year's QA. Adjustment to the new Python and Pandas versions. This year, we are doing a reverse process - first QA, then unit improvement.
  • Learning notebook
  • Example notebook
  • Exercise notebook
• To be released on:
  • SLU04 - SLU10 learning notebooks: 19 November 2023
  • SLU04 - SLU10 exercise notebooks: 26 November 2023
  • SLU11- SLU19 learning and exercise notebooks: 26 November 2023
• To be ready in November

SLU	Name	Last year instructor	Batch 7 instructor	Last year QA	Batch 7 QA
SLU04	Basic Stats with Pandas	@SaraOGomes	@cmm79	@jgomes959	@BG2602
SLU05	Covariance & Correlation	@kagglekim	@cmm79	@anaritarc	@BG2602
SLU06	Dealing with Data Problems	@majkah0	@TeignmouthElectron	@SaraOGomes	@BG2602
SLU07	Regression with Linear Regression	@jgerebelo	@joaogilsa	@carlacotas	@Mohamedgaber9
SLU08	Metrics for Regression	@marianahenriques1	@joaogilsa	@cd702	@Mohamedgaber9
SLU09	Classification with Logistic Regression	@majkah0	@majkah0	@carlacotas	@CaitlinHulse
SLU10	Metrics for Classification	@phgui	@majkah0	@majkah0	@CaitlinHulse
SLU11	Tree-Based Models	@anaritarc	@margaridantunes	@carlacotas	@Mohamedgaber9
SLU12	Feature Engineering (aka Real World Data)	@danizao	João Nobre	@anaritarc	@Mohamedgaber9
SLU13	Bias-Variance tradeoff & Model Selection	@jgerebelo	@rodrigomverissimo	@anaritarc	@BG2602
SLU14	Model complexity & Overfitting	@Gustavo-SF	@Gustavo-SF	@Jujulian3	@BG2602
SLU15	Hyperparameter Tuning	@jgomes959	@jgomes959	@SaraOGomes	@BG2602
SLU16	Workflow	@cimendes		@fabiocruz	@TeignmouthElectron
SLU17	Ethics & Fairness	@hershaw	@majkah0	@Gustavo-SF	@TeignmouthElectron
SLU18	Support Vector Machines (SVM) (optional unit)	@cimendes	@majkah0	@Jujulian3
SLU19	k-Nearest Neighbors (kNN) (optional unit)	@cimendes	@majkah0	@Jujulian3

Group	SLUs	Batch 7 QA lead*	Batch 7 backup QA**
QA1	SLU04, SLU05, SLU06	@BG2602	Caitlin Hulse
QA2	SLU07, SLU08	@Mohamedgaber9	Cora
QA3	SLU09, SLU10	Caitlin Hulse
QA4	SLU11, SLU12	@Mohamedgaber9
QA5	SLU13, SLU14, SLU15	@BG2602
QA6	SLU16, SLU17	@@TeignmouthElectron

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)

Bootcamp presentations

Bootcamp presentations will be split in two parts. Presentations will be given by senior instructors. This is what is expected from each instructor:

• The presentation should be <= 60 min including student questions. The presentation should be on concepts and insights for the given topic, not the technical implementation in Python.
• If possible, read the relevant learning notebooks and give a high level feedback on them (e.g. topic X is not relevant, you should add topic Y)
• If possible, answer questions from junior instructors who will update the corresponding SLUs, e.g. in a ~ 1 hour meeting where they will come with prepared questions.
  Time requirements: 1 hour for the talk + time to prepare the talk (we can provide slides from last year), <1 hour to read the learning notebooks, 1 hour for meeting with junior instructors.

Bootcamp part 1, Sunday 26. November 2023

• Instructor 1: LTPlabs
  • ~ 30 min: Intro to data science, SLU04 - Basic Stats with Pandas, SLU05 - Covariance and Correlation
  • ~ 30 min: SLU06 - Dealing with Data Problems
• Instructor 2: José Rebelo, EDP
  • 30 - 60 min: SLU07 - Regression with Linear Regression, SLU08 - Metrics for Regression
• Instructor 3: LTPlabs
  • 30 - 60 min, SLU09 - Classification with Logistic Regression, SLU10 - Metrics for Classification

Bootcamp part 2, Sunday 3. December 2023

• Instructor 4: João Ascensão, Stratio - TBC
  *45-60 min: SLU11 - Tree-Based Models, SLU12 - Feature Engineering

• Instructor 5: Maria Cristina Dominguez

  • ~60 min: SLU13 - Bias-Variance tradeoff & Model Selection, SLU14 - Model complexity and Overfitting, SLU15 - Hyperparameter Tuning

• Instructor 6: Sam Hopkins, DareData

  • 30-60 min: SLU16 - Workflow, SLU17 - Ethics and Fairness

• Hackathon 1

  • Come up with new problem for hackathon
  • Create description, Find dataset and create data splits (should require some iteration and validating the most common approaches work well, i.e., they improve over a dummy baseline)
  • Create baseline instructor solution
  • Evaluation guidelines doc
  • Overall guidelines for instructors to help out in hackathon

• To be released on 17 December 2023

• To be ready in November

Work unit	Name	Last year instructor	Batch 7 instructor	Last year QA	Batch 7 QA
Hackathon 1	Binary Classification	@wilsonramos1	@jgomes959	?	.

Specialization 2, 8 January 2024 - 4 February 2024

• Project manager: Kim Pronk @kagglekim

• Senior instructor:

  • 1 hour AMA session
  • If possible, read the relevant learning notebooks and give a high level feedback on them (e.g. topic X is not relevant, you should add topic Y)
  • If possible, answer questions from junior instructors who will update the corresponding SLUs, e.g. in a ~ 1 hour meeting where they will come with prepared questions.

• Time requirements: 1 hour for the talk + time to prepare the talk (we can provide slides from last year), <1 hour to read the learning notebooks, 1 hour for meeting with junior instructors.

• Junior instructors

  • Unit: minimum changes based on issues from last year and this year's QA. Adjustment to the new Python and Pandas versions. This year, we are doing a reverse process - first QA, then unit improvement.
  • In case of doubts, ask the senior instructor. Ideally, prepare all your questions, then meet with the senior instructor.

• To be released on 8 January (BLU01), 15 January (BLU02), 22 January (BLU03)

• To be ready in ** December 2023**

• Hackathon 2

  • Come up with new problem for hackathon
  • Create description, Find dataset and create data splits (should require some iteration and validating the most common approaches work well, i.e., they improve over a dummy baseline)
  • Create baseline instructor solution
  • Evaluation guidelines doc
  • Overall guidelines for instructors to help out in hackathon

• To be released on 4 February (Hackathon 02)

• To be ready in mid January

Work unit	Name	Last year instructor	Batch 7 instructor(s)	Last year QA
-	Spec lead	@martinb-bb
BLU01	Messy Data	@JerBouma		@majkah0
BLU02	Advanced Wrangling	@minhhoang1023		@cd702
BLU03	Data Sources	@jmaslek		@anaritarc
Hackathon 2	Data Wrangling	@martinb-bb @JerBouma @minhhoang1023 @DidierRLopes

• Batch 7 QA Lead BLU01/BLU02/BLU03: @AhmedEmad2525
• Batch 7 backup QA BLU01/BLU02/BLU03:

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)
Both QA people do the Hackathon verification

Specialization 3, 5 February - 3 March 2024

• Project manager: Mária Hanulová @majkah0

• Senior instructor: Telmo Felgueira, Loka / JungleAI

  • 1 hour AMA session
  • If possible, read the relevant learning notebooks and give a high level feedback on them (e.g. topic X is not relevant, you should add topic Y)
  • If possible, answer questions from junior instructors who will update the corresponding SLUs, e.g. in a ~ 1 hour meeting where they will come with prepared questions.

• Time requirements: 1 hour for the talk + time to prepare the talk (we can provide slides from last year), <1 hour to read the learning notebooks, 1 hour for meeting with junior instructors.

• Junior instructors

  • Unit: minimum changes based on issues from last year and this year's QA. Adjustment to the new Python and Pandas versions. This year, we are doing a reverse process - first QA, then unit improvement.
  • In case of doubts, ask the senior instructor. Ideally, prepare all your questions, then meet with the senior instructor.

• To be released on 5 February (BLU04), 12 February (BLU05), 19 February (BLU06)

• To be ready in January

• Hackathon 3

  • Come up with new problem for hackathon
  • Create description, Find dataset and create data splits (should require some iteration and validating the most common approaches work well, i.e., they improve over a dummy baseline)
  • Create baseline instructor solution
  • Evaluation guidelines doc
  • Overall guidelines for instructors to help out in hackathon

• To be released on 3 March (Hackathon 03)

• To be ready in mid February

Work unit	Name	Last year instructor	Batch 7 instructor(s)	Last year QA
-	Spec lead	@TSFelg	@TSFelg
BLU04	Time Series Concepts	@PedroRibeiro80		@Sonia-se
BLU05	Classical Time Series Models	@jgerebelo		@carlacotas
BLU06	Machine Learning for Time Series	@jdpsc	@TeignmouthElectron	@SaraOGomes
Hackathon 3	Timeseries	@TSFelg		@Gustavo-SF

• Batch 7 QA Lead BLU04/BLU05/BLU06: @Mohamedgaber9
• Batch 7 backup QA BLU04/BLU05/BLU06:

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)
Both QA people do the Hackathon verification

Specialization 4, 4 March - 31 March 2024

• Project manager:

• Senior instructor:

  • 1 hour AMA session
  • If possible, read the relevant learning notebooks and give a high level feedback on them (e.g. topic X is not relevant, you should add topic Y)
  • If possible, answer questions from junior instructors who will update the corresponding SLUs, e.g. in a ~ 1 hour meeting where they will come with prepared questions.

• Time requirements: 1 hour for the talk + time to prepare the talk (we can provide slides from last year), <1 hour to read the learning notebooks, 1 hour for meeting with junior instructors.

• Junior instructors

  • Unit: minimum changes based on issues from last year and this year's QA. Adjustment to the new Python and Pandas versions. This year, we are doing a reverse process - first QA, then unit improvement.
  • In case of doubts, ask the senior instructor. Ideally, prepare all your questions, then meet with the senior instructor.

• To be released on 4 March (BLU07), 11 March (BLU08), 18 March (BLU09)

• To be ready in February

• Hackathon 4

  • Come up with new problem for hackathon
  • Create description, Find dataset and create data splits (should require some iteration and validating the most common approaches work well, i.e., they improve over a dummy baseline)
  • Create baseline instructor solution
  • Evaluation guidelines doc
  • Overall guidelines for instructors to help out in hackathon

• To be released on 31 March (Hackathon 04)

• To be ready in ** mid March**

Work unit	Name	Last year instructor	Batch 7 instructor(s)	Last year QA
-	Spec lead	@CatarinaSilva
BLU07	Feature Extraction	@CatarinaSilva		@cd702
BLU08	Dimensionality Reduction	@CatarinaSilva		@majkah0
BLU09	Information Extraction	@CatarinaSilva		@carlacotas
Hackathon 4	NLP	BancoBPI

• Batch 7 QA Lead BLU07/BLU08/BLU09: @CaitlinHulse
• Batch 7 backup QA BLU07/BLU08/BLU09: @BG2602

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)
Both QA people do the Hackathon verification

Specialization 5 - this will be an optional specialization

• Project manager:
• Junior instructors
  • Unit: minimum changes based on issues from last year and this year's QA. Adjustment to the new Python and Pandas versions. This year, we are doing a reverse process - first QA, then unit improvement.
• To be released in March/April
• To be ready in March

Work unit	Name	Last year instructor	Batch 6 instructor(s)	Last year QA
-	Spec lead
BLU10	Non-personalised Recommender			@majkah0 @anaritarc
BLU11	Personalized Recommenders			@majkah0 @anaritarc
BLU12	Workflow			@majkah0 @anaritarc
Hackathon 5	Recommender Systems

• Batch 7 QA Lead BLU10/BLU11/BLU12: @TeignmouthElectron
• Batch 7 backup QA BLU10/BLU11/BLU12:

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)
Both QA people do the Hackathon verification

Specialization 6, 1 April - 28 April 2024

• Project manager:

• Senior instructor: Gustavo Fonseca, LDSA

  • 1 hour AMA session
  • If possible, read the relevant learning notebooks and give a high level feedback on them (e.g. topic X is not relevant, you should add topic Y)
  • If possible, answer questions from junior instructors who will update the corresponding SLUs, e.g. in a ~ 1 hour meeting where they will come with prepared questions.

• Time requirements: 1 hour for the talk + time to prepare the talk (we can provide slides from last year), <1 hour to read the learning notebooks, 1 hour for meeting with junior instructors.

• Junior instructors

  • Unit: minimum changes based on issues from last year and this year's QA. Adjustment to the new Python and Pandas versions. This year, we are doing a reverse process - first QA, then unit improvement.
  • In case of doubts, ask the senior instructor. Ideally, prepare all your questions, then meet with the senior instructor.

• To be released on 1 April (BLU13), 8 April (BLU14), 15 April (BLU15)

• To be ready in March

• Hackathon

  • Come up with new problem for hackathon
  • Create description, Find dataset and create data splits (should require some iteration and validating the most common approaches work well, i.e., they improve over a dummy baseline)
  • Create baseline instructor solution
  • Evaluation guidelines doc
  • Overall guidelines for instructors to help out in hackathon

• To be released on 28 April (Hackathon 06)

• To be ready in mid April

Extra session about Venture Capital: Armilar

Work unit	Name	Last year instructor	Batch 7 instructor(s)	Last year QA
-	Spec lead	@cimendes	@Gustavo-SF
BLU13	Basic model Deployment	@cimendes		@carlacotas
BLU14	Deployment in the real world	@cimendes
BLU15	Model CSI	@cimendes		@carlacotas
Hackathon 6	Data science in real world	@CatarinaSilva @cimendes @InesPessoa

• Batch 7 QA Lead BLU13/BLU14/BLU15:
• Batch 7 backup QA BLU13/BLU14/BLU15:

*It will be the responsible for checking the SLUs
** It will be the responsible for checking the SLUs, in case there are major changes (exercises or materials updates)
Both QA people do the Hackathon verification

Capstone, 29 April - 15 July 2024

• Preparing a strong dataset and problem
• Help building documents/forms/etc
• Replying to students QA
• Beta-testing/QAing
• Grading capstone
• To be released on 29 April
• To be ready in mid April

Work unit	Name	Last year instructor (s)	Batch 7 instructor(s)
-	Capstone	@minhhoang1023 @cimendes @fabiocruz @Gustavo-SF @anaritarc @majkah0

Other possible extra sessions:

• NOS (LLM, Data Science in Real World);
• AICEP (Classfication, Data Science in Real World);
• BPI (Classfication, Data Science in Real World)

[VascoMano]

I am available for QA for both the SLU2 and the admission test.

Note: I don't have access to this repository

[majkah0]

I'm in for SLU09 logistic regression and something else, maybe the optional SLUs. Also for the exam QA.
I would also be in for an optional hackathon training SLU, number 10.5 and I'd like to have Rita for QA for that 🙂 .
What will be the schedule for batch 7? If I could have one student success wish, it would be to change the bootcamp structure - 1 week longer, with lectures divided in two and 2 office hours. 😁 We have discussed this a bit during this year, but then no conclusions were made.

[cd702]

Hello, I would also like to know if there is already an update on the schedule?

[cd702]

I would be interested in QA for SLU06 or SLU08 as well as 12 or 13 but it is so quiet here i do not know if this is the right place...

[majkah0]

Hi @cd702 thank you, this is the right place! In fact, I was thinking about contacting you today :) I'm starting to bring in some life now. Would you also be in as instructor? We are doing just minimal maintenance this year, fixing the errors.

[cd702]

I will have another look at the units and let you know. Are the dates in the instructors repo confirmed?

[majkah0]

@cd702 Hi Cora, sorry for the late reply, the dates will be confirmed this weekend.

[jgomes959]

@majkah0 Hi Mária, I can still be responsible for SLU02 this year :)

[majkah0]

Perfect, thank you @jgomes959 . Just to warn you - Pandas has changed row subsetting, so there might be a lot of corrections to do.

[anaritarc]

Hi, doesn't Vasco already has a list of who will be responsible for each teaching?
Also maybe we should update it, per Spec, even for the bootcamp, and highlight the fact that the process has changes

[majkah0]

@cd702 Hi Cora, this is finally moving :) You were interested in QA for SLU 06, 08, 12 or 13. Can I sign you up for some of those? These units will go out at the end of November/beginning of December.
We are also looking for QA for the admissions units and test which will go out in mid October.
This year, there will be minimal unit development. The QA should happen first, then the instructor will correct all the issues. So you can already start, the instructor repo is set up.