Skip to main content

Course

Sampling in Python

Intermediate

4.7+

Updated 04/2025

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Start Course for Free

Included withPremium or Teams

PythonProbability & Statistics4 hours15 videos51 Exercises4,000 XP40,496Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

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.

Prerequisites

Introduction to Statistics in Python

1

Introduction to Sampling

Sampling and point estimates

Reasons for sampling

Simple sampling with pandas

Simple sampling and calculating with NumPy

Convenience sampling

Are findings from the sample generalizable?

Are these findings generalizable?

Pseudo-random number generation

Generating random numbers

Understanding random seeds

2

Sampling Methods

Simple random and systematic sampling

Simple random sampling

Systematic sampling

Is systematic sampling OK?

Stratified and weighted random sampling

Which sampling method?

Proportional stratified sampling

Equal counts stratified sampling

Weighted sampling

Cluster sampling

Benefits of clustering

Performing cluster sampling

Comparing sampling methods

3 kinds of sampling

Comparing point estimates

3

Sampling Distributions

Relative error of point estimates

Calculating relative errors

Relative error vs. sample size

Creating a sampling distribution

Replicating samples

Replication parameters

Approximate sampling distributions

Exact sampling distribution

Generating an approximate sampling distribution

Exact vs. approximate

Standard errors and the Central Limit Theorem

Population & sampling distribution means

Population & sampling distribution variation

4

Bootstrap Distributions

Introduction to bootstrapping

Principles of bootstrapping

With or without replacement?

Generating a bootstrap distribution

Comparing sampling and bootstrap distributions

Bootstrap statistics and population statistics

Sampling distribution vs. bootstrap distribution

Compare sampling and bootstrap means

Compare sampling and bootstrap standard deviations

Confidence intervals

Confidence interval interpretation

Calculating confidence intervals

Congratulations!

Sampling in Python

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

Don’t just take our word for it

*4.7

from 874 reviews

78%

19%

2%

0%

0%

Highest to Lowest
Lowest to Highest
Most recent
Top reviews

Devarajan

about 7 hours

IOANNIS

about 9 hours

文鈞

about 13 hours

Blessed

about 13 hours

Abdelrahman

about 12 hours

Duy Anh

about 11 hours

Devarajan

IOANNIS

文鈞

Join over 16 million learners and start Sampling in Python today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.