After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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
Curriculum
Course outline
1
Introduction to Sampling
Learn 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 XP
- Reasons for sampling50 XP
- Simple sampling with pandas100 XP
- Simple sampling and calculating with NumPy100 XP
- Convenience sampling50 XP
- Are findings from the sample generalizable?100 XP
- Are these findings generalizable?100 XP
- Pseudo-random number generation50 XP
- Generating random numbers100 XP
- Understanding 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.
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.
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.
Sampling in Python
Course
Complete
