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Sampling in Python

4.4+
60 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.

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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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  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

In the following tracks

Associate Data Scientist in PythonData Analyst with PythonStatistics Fundamentals with Python

Collaborators

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Dr. Chester Ismay
Collaborator's avatar
Amy Peterson
James Chapman HeadshotJames 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 60 reviews
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  • Tony F.
    8 months

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

  • Michael C.
    8 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.

  • Muhammad A.
    9 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.
    10 months

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

  • Ishan R.
    10 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."

Muhammad A.

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