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This weeks topic is on data science, finally somehting interesting. The guest lecturer are very knowledgeable, and the topics
+were very interesting. The topics were hard to understand at times, since it is real application of what we learned in say stat 135, which is just the theoretical
+part of the statistics. It is interesting to see how stat is done in actual science, from collecting data to deciding which tests to use, and how to corectly
+explain the result. It was really challenging since I was not familiar with a lot of statistical tests. I knew the math but was not
+at a familiarity that i can confortably apply the theory to answer the question. Im brushing up on the stats that I have learned,
+and truly trying to understand the implication of the theory. Math is not the most important part, but there are much deeper meaning in how to carry out statistical analysis.
+
+2. I did not have an ah ha moment, however, I have learn to think stat and science a different way. It used to be ok, here is a data, and here is a formula, lets plug it in and calculate
+the p value and there are basically a standard way of carrying out test. Now the question becomes how can we do it, what are the assumptions
+that are often ignored, how is the theoretical model different from the model, and what are some of the justification that we can actually carry out these tests.
+It is intersting to see the philosophical part of probability, and what it means to be frequencist. In terms of road blocks,there is not much to be said
+It was just a presentation, and not much actual work is being done.
+
+3. There is not much to change since there wer enot much of actual work being done. However, it is great to see a road map of
+what this class is going and how I should prepare for it. I think the most important lessen is to actually think when doing stat.
+Now just plug and chug, or apply the formula, but actually think what it implies, or the necesary justification to ensure that the tests are valid