Module 7: Understanding and Wrangling Data

This module provides an introduction to the interconnected nature of the data science pipeline. We consider what it means to pursue research goals and ask research questions effectively with data. Given that there are often many decisions involved in pursuing a beginning-to-end data science analysis, what are some best practices when it comes to communicating our research findings? Finally, what are some ways in which we might clean an manipulate a dataframe for further analysis?

Module 8: Linear Regression

This module introduces how a linear regression model can be used and evaluated for machine learning purposes. We discuss how to predict a numerical response variable given a set of numerical and/or categorical variables.

Module 9: Feature Selection and Cross-Validation Techniques

What does it mean to overfit a predictive model? How does an overfit model impact our our ability to pursue machine learning goals? One way to overfit a predictive model is by including too many explanatory varaibles that don't bring 'enough' predictive power to the model? In this section we explore ways of measuring whether or not an explanatory variable brings 'enough' predictive power to a predictive model. We also explore ways of attempting to find the optimal combination of explanatory variables that best meet our machine learning goals for a predictive model.

Module 10: Logistic Regression and Classification

In this module we introduce the logistic regression model which is one of the most common models for predicting a categorical response variable with two distinct values. We discuss how to fit and evaluate a logistic regression model for machine learning purposes. Furthermore, we discuss how to use a logistic regression model as a classifier. We discuss how to evaluate the performance of a classifier model. Finally, we implement the features selection techniques that we introduced in module 9 to attempt to find the optimal combination of explanatory variables to use that yields the best classifier performance for machine learning purposes.

Module 11: More Machine Learning Methods

Module 12: Populations, Samples, and Statistics

Module 13: Statistical Inference for Populations