FeaturedUdemy
Sampling in Python
4.8(3,783)54k studentsINTERMEDIATEEnglish
Korshub's take
Course Overview
The Sampling in Python course explores foundational techniques for statistical inference and experimental design without surveying entire populations. Working with real-world datasets like coffee ratings, Spotify track data, and employee attrition, you will learn simple random, stratified, and cluster sampling, as well as bootstrap distributions for estimating population parameters.
Who Is This Course For?
Aimed at intermediate data analysts, statisticians, and data scientists using Python to perform statistical inference and quantify uncertainty in sample metrics.
Key Takeaways
- Apply simple random, systematic, stratified, and cluster sampling strategies in Python.
- Calculate point estimates and quantify sampling bias across different sampling methods.
- Generate sampling distributions to analyze standard errors and the Central Limit Theorem.
- Perform bootstrap resampling to construct confidence intervals for population parameters.
About this course
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.
Price
Free
Advertisement