Introduction to Data in R
Learn the language of data, study types, sampling strategies, and experimental design.
- 4 hours
- 15 Videos
- 46 Exercises
- 82,989 Participants
- 3,200 XP
Create Your Free Account
By pressing Continue you accept the Terms of Use and Privacy Policy. You also accept that you are aware that your data will be stored outside of the EU and that you are above the age of 16.
Loved by learners at thousands of top companies:
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
Language of data
FreeThis chapter introduces terminology of datasets and data frames in R.
-
Sampling strategies and experimental design
This chapter defines various sampling strategies and their benefits/drawbacks as well as principles of experimental design.
-
Study types and cautionary tales
In this chapter, you will learn about observational studies and experiments, scope of inference, and Simpson's paradox.
-
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.
-
1
Language of data
FreeThis chapter introduces terminology of datasets and data frames in R.
-
Study types and cautionary tales
In this chapter, you will learn about observational studies and experiments, scope of inference, and Simpson's paradox.
-
Sampling strategies and experimental design
This chapter defines various sampling strategies and their benefits/drawbacks as well as principles of experimental design.
-
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.
What do other learners have to say?
“I've used other sites, but DataCamp's been the one that I've stuck with.”
Devon Edwards Joseph
Lloyd's Banking Group
“DataCamp is the top resource I recommend for learning data science.”
Louis Maiden
Harvard Business School
“DataCamp is by far my favorite website to learn from.”
Ronald Bowers
Decision Science Analytics @ USAA
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.
See MoreCollaborators
-
Nick Carchedi
-
Tom Jeon