Lecture Overview
Schedule
| Title | Description | Recording |
|---|---|---|
| Learn to use a programming language and Jupyter notebooks as you walk through a real world data Science application that includes downloading data from the web, wrangling the data into a useable format and creating an effective data visualization. | ||
| Learn to read in various cases of data sets locally and from the web. Once read in, these data sets will be used to walk through a real world data science application that includes wrangling the data into a useable format and creating an effective data visualization. | ||
| This week will be centered around tools for cleaning and wrangling data. Again, this will be in the context of a real world data science application and we will continue to practice working through a whole case study that includes downloading data from the web, wrangling the data into a useable format and creating an effective data visualization. | ||
| Expand your data visualization knowledge and tool set beyond what we have seen and practiced so far. We will move beyond scatter plots and learn other effective ways to visualize data, as well as some general rules of thumb to follow when creating visualizations. All visualization tasks this week will be applied to real world data sets. Again, this will be in the context of a real world data science application and we will continue to practice working through a whole case study that includes downloading data from the web, wrangling the data into a useable format and creating an effective data visualization. | ||
| This chapter will introduce the concept of using version control systems to track changes to a project over its lifespan, to share and edit code in a collaborative team, and to distribute the finished project to its intended audience. This chapter will also introduce how to use the two most common version control tools: Git for local version control, and GitHub for remote version control. We will focus on the most common version control operations used day-to-day in a standard data science project. There are many user interfaces for Git; in this chapter we will cover the Jupyter Git interface. | ||
| This chapter and the next together serve as our first foray into answering predictive questions about data. In particular, we will focus on classification, i.e., using one or more variables to predict the value of a categorical variable of interest. This chapter will cover the basics of classification, how to preprocess data to make it suitable for use in a classifier, and how to use our observed data to make predictions. The next chapter will focus on how to evaluate how accurate the predictions from our classifier are, as well as how to improve our classifier (where possible) to maximize its accuracy. | ||
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7. Classification, continued coming soon |
This chapter continues the introduction to predictive modeling through classification. While the previous chapter covered training and data preprocessing, this chapter focuses on how to evaluate the performance of a classifier, as well as how to improve the classifier (where possible) to maximize its accuracy. | |
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8. Regression coming soon |
This chapter continues our foray into answering predictive questions. Here we will focus on predicting numerical variables and will use regression to perform this task. This is unlike the past two chapters, which focused on predicting categorical variables via classification. However, regression does have many similarities to classification: for example, just as in the case of classification, we will split our data into training, validation, and test sets, we will use scikit-learn workflows, we will use a K-nearest neighbors (KNN) approach to make predictions, and we will use cross-validation to choose K. We will focus on prediction in cases where there is a response variable of interest and a single explanatory variable. | |
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9. Regression, continued coming soon |
Up to this point, we have solved all of our predictive problems—both classification and regression—using K-nearest neighbors (KNN)-based approaches. In the context of regression, there is another commonly used method known as linear regression. This chapter provides an introduction to the basic concept of linear regression, shows how to use scikit-learn to perform linear regression in Python, and characterizes its strengths and weaknesses compared to KNN regression. The focus is, as usual, on the case where there is a single predictor and single response variable of interest; but the chapter concludes with an example using multivariable linear regression when there is more than one predictor. | |
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10. Clustering coming soon |
As part of exploratory data analysis, it is often helpful to see if there are meaningful subgroups (or clusters) in the data. This grouping can be used for many purposes, such as generating new questions or improving predictive analyses. This chapter provides an introduction to clustering using the K-means algorithm, including techniques to choose the number of clusters. | |
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11. Introduction to statistical inference coming soon |
A typical data analysis task in practice is to draw conclusions about some unknown aspect of a population of interest based on observed data sampled from that population; we typically do not get data on the entire population. Data analysis questions regarding how summaries, patterns, trends, or relationships in a data set extend to the wider population are called inferential questions. This chapter will start with the fundamental ideas of sampling from populations and then introduce two common techniques in statistical inference: point estimation and interval estimation. | |
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12. Introduction to statistical inference, continued coming soon |
Unfortunately, we cannot construct the exact sampling distribution without full access to the population. However, if we could somehow approximate what the sampling distribution would look like for a sample, we could use that approximation to then report how uncertain our sample point estimate is (as we did above with the exact sampling distribution). There are several methods to accomplish this; in this course, we will use the bootstrap. We will discuss interval estimation and construct confidence intervals using just a single sample from a population. A confidence interval is a range of plausible values for our population parameter. |
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