Sampling in R

Master sampling to get more accurate statistics with less data.
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4 Hours15 Videos51 Exercises2,495 Learners
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Course Description

Sampling is a cornerstone of inference statistics and hypothesis testing. It's tremendously important in survey analysis and experimental design. This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions. Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition.

  1. 1

    Bias Any Stretch of the Imagination

    Free
    Learn what sampling is and why it is useful, understand the problems caused by convenience sampling, and learn about the differences between true randomness and pseudo-randomness.
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  2. 2

    Don't get theory eyed

    Learn how to and when to perform the four methods of random sampling: simple, systematic, stratified, and cluster.
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  3. 3

    The n's justify the means

    Learn how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.
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  4. 4

    Pull Your Data Up By Its Bootstraps

    Learn how to use resampling to perform bootstrapping, used to estimate variation in an unknown population. Understand the difference between sampling distributions and bootstrap distributions.
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In the following tracks
StatisticianStatistics Fundamentals
Collaborators
Chester Ismay
Richie Cotton Headshot

Richie Cotton

Curriculum Architect at DataCamp
Richie is a Learning Solutions Architect at DataCamp. He has been using R since 2004, in the fields of proteomics, debt collection, and chemical health and safety. He has released almost 30 R packages on CRAN and Bioconductor – most famously the assertive suite of packages – as well as creating and contributing to many others. He also has written two books on R programming, Learning R and Testing R Code.
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