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Inferential Statistics

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data

Created by Duke University


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What you’ll learn


From this learning opportunity you can acquire the compentencies sought from companies these days. The most in demand technique in the learning resource that is frequently mentioned from organizations is Data Analysis. The most in demand tool is R.

Who will benefit?


Evaluating the description from this educational opportunity with nearly 10,000 data-related job maps, we determine that those in or pursuing Data Scientist roles have the most to gain.