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Serdar Balci has completed

# Introduction to Data in R

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4 hours
3,200 XP

## 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.
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1. 1

### Language of data

Free

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

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Welcome to the course!
50 xp
Loading data into R
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.

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Datasets

Course evaluationUC Berkeley admissionsUS state regions

Collaborators

Mine Cetinkaya-Rundel

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

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