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CST 383 Week 7

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 This was our seventh week in CST 383 - Data Science. Reflection This week we reviewed ordinal and nominal (dummy variables or one hot) encoding. We discussed a couple of ways to do each in code, and when we want to use one type of encoding over the other. Although we have covered this in previous weeks, it was still helpful to review the topic, as we had to use it during this week's lab. We also discussed logistic regression, which is similar in concept to linear regression but is designed specifically to predict binary, categorical variables. Although linear regression can be made to do this (for example, if we consider all predictions above 0.5 to be True), logistic regression is generally better at it. Although we didn't have much of a chance to explore the regression model during the lab, it appears to be a powerful tool. In one of our textbooks, we also covered in more detail how linear and logistic models are trained. We also covered another similar model, the polynomial...
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CST 383 Week 6

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This was our sixth week in CST 383 - Data Science.  Reflection This week we continued to focus on hyperparameters, which we begun discussing last week. We also covered KNN and Linear regression models, and how we can asses the accuracy of regression models using MSE, RMSE, and MAE. Going through the lecture, the concepts seemed to be relatively straightforward. I didn't feel confused about any of the concepts going into the homework. This feeling continued into the homework. While the portions of the homework that covered this week's concepts weren't necessarily easy, none of them were problematic. One homework topic that gave me problems was One Hot Encoding. We learned about encoding last week, but last week's homework didn't really include it. This week's homework did, and it proved to be the most challenging part by far. It felt like either the reading material wasn't fully explaining how to use the OneHotEncoding object properly, or that I simply wasn...
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CST 383 Week 5

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This was our fifth week in CST 383 - Data Science. Reflection This week we covered how to process missing data in datasets, how to scale datasets, and the basics of training and testing machine learning algorithms. Some of the more straightforward concepts this week were those about missing data. Although much of the information itself was new, it corresponded the most with what we've learned about DataFrames and other data types in previous weeks. I would say that the most difficult concepts covered this week were those about training and testing algorithms. We covered some basic ideas around this in the first weeks of the course, but this was the first time that we covered how this is done in code. Despite the material being difficult, enough was covered in class to greatly help with the homework that covered these concepts. I imagine that we will expand on these concepts in upcoming weeks. Class Lecture  Missing Data This week we discussed missing and bad data within data sets. ...
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CST 383 Week 4

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This was our fourth week in CST 383 - Data Science.  Reflection This week we went into more detail on crosstabs, how to manipulate them, and how to plot them. We also briefly covered how to plot more than two variables on a scatter plot and Violin Plots and Seaborn Facet Grids. After the second week, which introduced crosstabs but did not cover them in detail, I was wondering when we were going to cover them in more detail. At first crosstabs seemed like a unique data structure, but after learning about them and manipulating them in code they feel like any other Dataframe or 2D structure. Speaking of 2D structures, at the beginning of this course I found manipulating 2D arrays and Dataframes to be quite complex. I often had problems envisioning the data that I was working on. After having worked on 2D structures for the past few weeks, I am happy to find that it now feels easier. While the syntax for crosstabs is more complicated, I was able to intuit the data easier than I was abl...
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