# Introduction to Data in R

Learn the language of data, study types, sampling strategies, and experimental design.

4 Hours15 Videos46 Exercises100,657 Learners3200 XP

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## Course Description

Scientists seek to answer questions using rigorous methods and careful observations. These observations—collected from the likes of field notes, surveys, and experiments—form the backbone of a statistical investigation and are called data. Statistics is the study of how best to collect, analyze, and draw conclusions from data. It is helpful to put statistics in the context of a general process of investigation: 1) identify a question or problem; 2) collect relevant data on the topic; 3) analyze the data; and 4) form a conclusion. In this course, you'll focus on the first two steps of the process.

1. 1

### Language of data

Free

This chapter introduces terminology of datasets and data frames in R.

Welcome to the course!
50 xp
100 xp
Types of variables
50 xp
Identify variable types
100 xp
Categorical data in R: factors
50 xp
Filtering based on a factor
100 xp
Complete filtering based on a factor
100 xp
Discretize a variable
50 xp
Discretize a different variable
100 xp
Combining levels of a different factor
100 xp
Visualizing numerical data
50 xp
Visualizing numerical and categorical data
100 xp
2. 2

### Study types and cautionary tales

In this chapter, you will learn about observational studies and experiments, scope of inference, and Simpson's paradox.

3. 3

### Sampling strategies and experimental design

This chapter defines various sampling strategies and their benefits/drawbacks as well as principles of experimental design.

4. 4

### Case study

Apply terminology, principles, and R code learned in the first three chapters of this course to a case study looking at how the physical appearance of instructors impacts their students' course evaluations.

Datasets

Course evaluationUC Berkeley admissionsUS state regions

Collaborators

Nick CarchediTom Jeon

#### Mine Cetinkaya-Rundel

Associate Professor at Duke University & Data Scientist and Professional Educator at RStudio

Mine is the Director of Undergraduate Studies and an Associate Professor of the Practice in the Department of Statistical Science at Duke University as well as a Professional Educator at RStudio. Her work focuses on innovation in statistics pedagogy, with an emphasis on computation, reproducible research, open-source education, and student-centered learning. She is the author of three open-source introductory statistics textbooks as part of the OpenIntro project and teaches the popular Statistics with R MOOC on Coursera.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden