Experimental Design in R
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
- 4 hours
- 12 Videos
- 52 Exercises
- 2,481 Participants
- 4,400 XP
Create Your Free Account
By continuing you accept the Terms of Use and Privacy Policy, that your data will be stored outside of the EU, and that you are 16 years or older.
Loved by learners at thousands of top companies:
Course Description
Experimental design is a crucial part of data analysis in any field, whether you work in business, health or tech. If you want to use data to answer a question, you need to design an experiment! In this course you will learn about basic experimental design, including block and factorial designs, and commonly used statistical tests, such as the t-tests and ANOVAs. You will use built-in R data and real world datasets including the CDC NHANES survey, SAT Scores from NY Public Schools, and Lending Club Loan Data. Following the course, you will be able to design and analyze your own experiments!
-
1
Introduction to Experimental Design
FreeAn introduction to key parts of experimental design plus some power and sample size calculations.
-
Randomized Complete (& Balanced Incomplete) Block Designs
Use the NHANES data to build a RCBD and BIBD experiment, including model validation and design tips to make sure the BIBD is valid.
-
Basic Experiments
Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.
-
Latin Squares, Graeco-Latin Squares, & Factorial experiments
Evaluate the NYC SAT scores data and deal with its missing values, then evaluate Latin Square, Graeco-Latin Square, and Factorial experiments.
-
1
Introduction to Experimental Design
FreeAn introduction to key parts of experimental design plus some power and sample size calculations.
-
Basic Experiments
Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.
-
Randomized Complete (& Balanced Incomplete) Block Designs
Use the NHANES data to build a RCBD and BIBD experiment, including model validation and design tips to make sure the BIBD is valid.
-
Latin Squares, Graeco-Latin Squares, & Factorial experiments
Evaluate the NYC SAT scores data and deal with its missing values, then evaluate Latin Square, Graeco-Latin Square, and Factorial experiments.
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
kaelen medeiros
Content Quality Analyst at DataCamp
Kaelen is a Content Quality Analyst at DataCamp. They have served as a co-organizer for the R-Ladies Chicago Meetup and are still involved with R-Ladies Global. Kaelen received a MS in Biostatistics from Louisiana State University Health Sciences Center, where they studied and worked at the Louisiana Tumor Registry and they've designed experiments (and more!) for the American College of Surgeons, HERE Technologies, and HealthLabs. If you meet them, you will undoubtedly hear about their cat, Scully, within the first 3 minutes. Other favorite topics include aliens, popcorn, podcasts, and nail polish.
See MoreCollaborators
-
Richie Cotton
-
Becca Robins