In this course you’ll learn about basic experimental design, a crucial part of any data analysis.

DescriptionChaptersExercisesInstructor

This online course about Experimental Design in R covers a key part of what a future data analyst would require.

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!

Enroll now in this Experimental Design in R course, and don’t miss the opportunity of learning with the best, as kaelen medeiros is. With 52 enriching exercises, 12 videos, and an estimated time of 4 hours to successfully end up the course, you will become one of the best.

Requisites before you start
Chapter 1: Introduction to Experimental Design
An introduction to key parts of experimental design plus some power and sample size calculations.
Chapter 2: 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.
Chapter 3: Basic Experiments
Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.
Chapter 4: 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.
Chapter 5: Introduction to Experimental Design
An introduction to key parts of experimental design plus some power and sample size calculations.
Chapter 6: Basic Experiments
Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.
Chapter 7: 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.
Chapter 8: 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.
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kaelen medeiros

Content Quality Analyst at DataCamp

Kaelen is a Content Quality Analyst at DataCamp and a data scientist at HealthLabs. 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 and HERE Technologies. 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.

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