Sampling in Python
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
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Course Description
Sampling in Python is the cornerstone of inference statistics and hypothesis testing. It's a powerful skill used in survey analysis and experimental design to draw conclusions without surveying an entire population. In this Sampling in Python course, you’ll discover when to use sampling and how to perform common types of sampling—from simple random sampling to more complex methods like stratified and cluster sampling. Using real-world datasets, including coffee ratings, Spotify songs, and employee attrition, you’ll learn to estimate population statistics and quantify uncertainty in your estimates by generating sampling distributions and bootstrap distributions.
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Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Statistics Fundamentals in Python
Go To Track- 1
Introduction to Sampling
FreeLearn what sampling is and why it is so powerful. You’ll also learn about the problems caused by convenience sampling and the differences between true randomness and pseudo-randomness.
Sampling and point estimates50 xpReasons for sampling50 xpSimple sampling with pandas100 xpSimple sampling and calculating with NumPy100 xpConvenience sampling50 xpAre findings from the sample generalizable?100 xpAre these findings generalizable?100 xpPseudo-random number generation50 xpGenerating random numbers100 xpUnderstanding random seeds100 xp - 2
Sampling Methods
It’s time to get hands-on and perform the four random sampling methods in Python: simple, systematic, stratified, and cluster.
Simple random and systematic sampling50 xpSimple random sampling100 xpSystematic sampling100 xpIs systematic sampling OK?100 xpStratified and weighted random sampling50 xpWhich sampling method?100 xpProportional stratified sampling100 xpEqual counts stratified sampling100 xpWeighted sampling100 xpCluster sampling50 xpBenefits of clustering50 xpPerforming cluster sampling100 xpComparing sampling methods50 xp3 kinds of sampling100 xpComparing point estimates100 xp - 3
Sampling Distributions
Let’s test your sampling. In this chapter, you’ll discover how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.
Relative error of point estimates50 xpCalculating relative errors100 xpRelative error vs. sample size50 xpCreating a sampling distribution50 xpReplicating samples100 xpReplication parameters50 xpApproximate sampling distributions50 xpExact sampling distribution100 xpGenerating an approximate sampling distribution100 xpExact vs. approximate50 xpStandard errors and the Central Limit Theorem50 xpPopulation & sampling distribution means100 xpPopulation & sampling distribution variation100 xp - 4
Bootstrap Distributions
You’ll get to grips with resampling to perform bootstrapping and estimate variation in an unknown population. You’ll learn the difference between sampling distributions and bootstrap distributions using resampling.
Introduction to bootstrapping50 xpPrinciples of bootstrapping100 xpWith or without replacement?100 xpGenerating a bootstrap distribution100 xpComparing sampling and bootstrap distributions50 xpBootstrap statistics and population statistics50 xpSampling distribution vs. bootstrap distribution100 xpCompare sampling and bootstrap means100 xpCompare sampling and bootstrap standard deviations100 xpConfidence intervals50 xpConfidence interval interpretation50 xpCalculating confidence intervals100 xpCongratulations!50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Statistics Fundamentals in Python
Go To Trackcollaborators
prerequisites
Introduction to Statistics in Python
James ChapmanCurriculum Manager, DataCamp
James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.
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