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Sampling in Python

4.4+
67 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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In the following Tracks

Certification Available

Data Analyst in Python

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Certification Available

Associate Data Scientist in Python

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Statistics Fundamentals in Python

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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
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Data Analyst in Python

Go To Track
Certification Available

Associate Data Scientist in Python

Go To Track

Statistics Fundamentals in Python

Go To Track

datasets

Coffee ratingsSpotify song attributesEmployee attrition

collaborators

Collaborator's avatar
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 67 reviews
70%
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  • muhammad t.
    4 days

    Great

  • Urich K.
    about 2 months

    Complex but very interesting

  • Noel C.
    3 months

    Excellent course! five stars

  • Li D.
    3 months

    Great course - a must. Very useful.

  • Maliheh B.
    4 months

    The course was excellent. However, I believe it could be enhanced by including an introductory section. Similar to the wrap-up in the final video of each course, an introduction at the beginning that outlines the course objectives, provides an overview of each section, and explains how all the parts are interconnected would give learners a clearer understanding of the course structure and its focus, and therefore would improve the learning process

"Great"

muhammad t.

"Complex but very interesting"

Urich K.

"Excellent course! five stars"

Noel C.

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