Skip to main content
HomeRModeling with Data in the Tidyverse

Modeling with Data in the Tidyverse

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

Start Course for Free
4 hours17 videos49 exercises23,639 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies


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.
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more
Try DataCamp for BusinessFor a bespoke solution book a demo.

In the following Tracks

Tidyverse Fundamentals in R

Go To Track
  1. 1

    Introduction to Modeling

    Free

    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.

    Play Chapter Now
    Background on modeling for explanation
    50 xp
    Exploratory visualization of age
    100 xp
    Numerical summaries of age
    100 xp
    Background on modeling for prediction
    50 xp
    Exploratory visualization of house size
    100 xp
    Log10 transformation of house size
    100 xp
    The modeling problem for explanation
    50 xp
    EDA of relationship of teaching & "beauty" scores
    100 xp
    Correlation between teaching and "beauty" scores
    100 xp
    The modeling problem for prediction
    50 xp
    EDA of relationship of house price and waterfront
    100 xp
    Predicting house price with waterfront
    100 xp
  2. 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.

    Play Chapter Now
  3. 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.

    Play Chapter Now
  4. 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".

    Play Chapter Now
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Tidyverse Fundamentals in R

Go To Track

collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Sumedh Panchadhar
Collaborator's avatar
Benjamin Feder

prerequisites

Data Manipulation with dplyr
Albert Y. Kim HeadshotAlbert Y. Kim

Associate Professor of Statistical & Data Sciences, Smith College

See More

What do other learners have to say?

Join over 14 million learners and start Modeling with Data in the Tidyverse today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.