FeaturedUdemy
Experimental Design in R
4.7(348)20k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate R course bridges statistical theory and practical experimentation, teaching you how to structure, run, and evaluate experiments across business, health, and tech domains. By moving beyond observational analysis, it empowers you to draw clear causal conclusions from real-world data.
Who It's For
Data analysts, researchers, and data scientists using R who need to design reliable experiments, select the appropriate statistical tests, and extract actionable insights.
Key Takeaways
- Experimental Structures: Learn how to formulate hypothesis-driven setups, including randomized block and factorial designs.
- Statistical Testing: Master standard hypothesis tests such as t-tests and ANOVAs to evaluate treatment effects.
- Real-World Datasets: Work with practical data including the CDC NHANES survey, NY Public School SAT scores, and Lending Club loan data.
About this course
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!
Price
Free
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