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Hypothesis Testing in Python

4.0+
69 reviews
Intermediate

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

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4 Hours15 Videos50 Exercises
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Course Description

Hypothesis testing lets you answer questions about your datasets in a statistically rigorous way. In this course, you'll grow your Python analytical skills as you learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. Working with real-world data, including Stack Overflow user feedback and supply-chain data for medical supply shipments, you'll gain a deep understanding of how these tests work and the key assumptions that underpin them. You'll also discover how non-parametric tests can be used to go beyond the limitations of traditional hypothesis tests.
  1. 1

    Introduction to Hypothesis Testing

    Free

    How does hypothesis testing work and what problems can it solve? To find out, you’ll walk through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-values, and false negative and false positive errors.

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    Hypothesis tests and z-scores
    50 xp
    Uses of A/B testing
    50 xp
    Calculating the sample mean
    100 xp
    Calculating a z-score
    100 xp
    p-values
    50 xp
    Criminal trials and hypothesis tests
    50 xp
    Left tail, right tail, two tails
    100 xp
    Calculating p-values
    100 xp
    Statistical significance
    50 xp
    Decisions from p-values
    50 xp
    Calculating a confidence interval
    100 xp
    Type I and type II errors
    100 xp
  2. 3

    Proportion Tests

    Now it’s time to test for differences in proportions between two groups using proportion tests. Through hands-on exercises, you’ll extend your proportion tests to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

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In the following tracks

Associate Data Scientist in PythonData Analyst with PythonStatistics Fundamentals with Python

Collaborators

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Dr. Chester Ismay
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Amy Peterson
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Izzy Weber

Prerequisites

Sampling in Python
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

*4.0
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  • Ruben N.
    4 months

    Good material, very professional explanations and the subject is totally applicable in real life work.

  • Alan S.
    4 months

    The course was very well designed and clearly explained. The examples were very helpful to fully understand the subject.

  • Valentino M.
    8 months

    Amazing as always! You will learn a lot of useful skills and sharp your statistical knowledge and programming abilities in the most market-demanded programming language.

  • Edwin A.
    8 months

    I recommend this course for those who want learn about hypothesis testing in Python.

  • Juan-Carlos V.
    8 months

    Moderate to strong developed course. The topics are difficult to swallow in 4 hours, but the descriptions are clear enough to follow the basics of concepts.

"Good material, very professional explanations and the subject is totally applicable in real life work."

Ruben N.

"The course was very well designed and clearly explained. The examples were very helpful to fully understand the subject."

Alan S.

"Amazing as always! You will learn a lot of useful skills and sharp your statistical knowledge and programming abilities in the most market-demanded programming language."

Valentino M.

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