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# Sampling in Python

4.4+
61 reviews
Intermediate

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

4 Hours15 Videos51 Exercises

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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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### In the following Tracks

Certification Available

#### Data Analyst with Python

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Certification Available

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1. 1

### Introduction to Sampling

Free

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.

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Sampling and point estimates
50 xp
Reasons for sampling
50 xp
Simple sampling with pandas
100 xp
Simple sampling and calculating with NumPy
100 xp
Convenience sampling
50 xp
Are findings from the sample generalizable?
100 xp
Are these findings generalizable?
100 xp
Pseudo-random number generation
50 xp
Generating random numbers
100 xp
Understanding random seeds
100 xp
2. 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. 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. 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.

### In the following Tracks

Certification Available

#### Data Analyst with Python

Go To Track
Certification Available

Go To Track

#### Statistics Fundamentals with Python

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Datasets

Coffee ratingsSpotify song attributesEmployee attrition

Collaborators

James Chapman

Curriculum 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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## Don’t just take our word for it

*4.4
from 61 reviews
69%
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• Tony F.
9 months

A very good course, it gives you the necessary basis to get started in statistics.

• Michael C.
10 months

Course appeared to be complete and with an appropriate level of rigor for me. Teacher was obviously of great quality. Needs to have an associated practice module. Thanks.

10 months

This is a good and important course. I think it would be better if some theoratical knowlege is added more but it may be biased because of my strong STEM background.

• Edwin A.
11 months

This is a recommended course to learn about sampling in Python.

• Ishan R.
12 months

Very good content!

"A very good course, it gives you the necessary basis to get started in statistics."

Tony F.

"Course appeared to be complete and with an appropriate level of rigor for me. Teacher was obviously of great quality. Needs to have an associated practice module. Thanks."

Michael C.

"This is a good and important course. I think it would be better if some theoratical knowlege is added more but it may be biased because of my strong STEM background."