Loved by learners at thousands of companies
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.
Bias Any Stretch of the ImaginationFree
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.Living the sample life50 xpReasons for sampling50 xpSimple sampling with dplyr100 xpSimple sampling with base-R100 xpA little too convenient50 xpAre findings from the sample generalizable?100 xpAre the findings generalizable? 2100 xpHow does Sue do sampling?50 xpGenerating random numbers100 xpUnderstanding random seeds100 xp
Don't get theory eyed
Learn how to and when to perform the four methods of random sampling: simple, systematic, stratified, and cluster.Simple is as simple does50 xpSimple random sampling100 xpSystematic sampling100 xpIs systematic sampling OK?100 xpCan't get no stratisfaction50 xpWhich sampling method?100 xpProportional stratified sampling100 xpEqual counts stratified sampling100 xpWeighted sampling100 xpWhat a cluster ...50 xpBenefits of clustering50 xpCluster sampling100 xpStraight to the point (estimate)50 xp3 kinds of sampling100 xpSummary statistics on different kinds of sample100 xp
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.An ample sample50 xpCalculating relative errors100 xpRelative error vs. sample size50 xpBaby back dist-rib-ution50 xpReplicating samples100 xpReplication parameters50 xpBe our guess, put our samples to the test50 xpExact sampling distribution100 xpApproximate sampling distribution100 xpExact vs. approximate50 xpErr on the side of Gaussian50 xpPopulation & sampling distribution means100 xpPopulation and sampling distribution variation100 xp
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.This bears a striking resample-lance50 xpPrinciples of bootstrapping100 xpWith or without replacement100 xpGenerating a bootstrap distribution100 xpA breath of fresh error50 xpBootstrap statistics and population statistics50 xpSampling distribution vs. bootstrap distribution100 xpCompare sampling and bootstrap means100 xpCompare sampling and bootstrap standard deviations100 xpVenus infers50 xpConfidence interval interpretation50 xpCalculating confidence intervals100 xpCongratulations50 xp
CollaboratorsDr. Chester Ismay
PrerequisitesIntroduction to Statistics in R
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.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA