After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
In this course, you will learn to model with data. Models attempt to capture the relationship between an outcome variable of interest and a series of explanatory/predictor variables. Such models can be used for both explanatory purposes, e.g. "Does knowing professors' ages help explain their teaching evaluation scores?", and predictive purposes, e.g., "How well can we predict a house's price based on its size and condition?" You will leverage your tidyverse skills to construct and interpret such models. This course centers around the use of linear regression, one of the most commonly-used and easy to understand approaches to modeling. Such modeling and thinking is used in a wide variety of fields, including statistics, causal inference, machine learning, and artificial intelligence.
Prerequisites
Curriculum
Course outline
1
Introduction to Modeling
This chapter will introduce you to some background theory and terminology for modeling, in particular, the general modeling framework, the difference between modeling for explanation and modeling for prediction, and the modeling problem. Furthermore, you'll start performing your first exploratory data analysis, a crucial first step before any formal modeling.
- Background on modeling for explanation50 XP
- Exploratory visualization of age100 XP
- Numerical summaries of age100 XP
- Background on modeling for prediction50 XP
- Exploratory visualization of house size100 XP
- Log10 transformation of house size100 XP
- The modeling problem for explanation50 XP
- EDA of relationship of teaching & "beauty" scores100 XP
- Correlation between teaching and "beauty" scores100 XP
- The modeling problem for prediction50 XP
- EDA of relationship of house price and waterfront100 XP
- Predicting house price with waterfront100 XP
2
Modeling with Basic Regression
Equipped with your understanding of the general modeling framework, in this chapter, we'll cover basic linear regression where you'll keep things simple and model the outcome variable y as a function of a single explanatory/ predictor variable x. We'll use both numerical and categorical x variables. The outcome variable of interest in this chapter will be teaching evaluation scores of instructors at the University of Texas, Austin.
3
Modeling with Multiple Regression
In the previous chapter, you learned about basic regression using either a single numerical or a categorical predictor. But why limit ourselves to using only one variable to inform your explanations/predictions? You will now extend basic regression to multiple regression, which allows for incorporation of more than one explanatory or one predictor variable in your models. You'll be modeling house prices using a dataset of houses in the Seattle, WA metropolitan area.
4
Model Assessment and Selection
In the previous chapters, you fit various models to explain or predict an outcome variable of interest. However, how do we know which models to choose? Model assessment measures allow you to assess how well an explanatory model "fits" a set of data or how accurate a predictive model is. Based on these measures, you'll learn about criteria for determining which models are "best".
R
Modeling with Data in the Tidyverse
Course
Complete
