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Hypothesis Testing with Python

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Overview

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In this course, you'll learn to plan, implement, and interpret a hypothesis test in Python. Hypothesis testing is used to address questions about a population based on a subset from that population. For example, A/B testing is a framework for learning about consumer behavior based on a small sample of consumers.

This course assumes some preexisting knowledge of Python, including the NumPy and pandas libraries.

Syllabus

  • Introduction to Hypothesis Testing: Find out what you'll learn in this course and why it's important.
    • Informational: Introduction to Hypothesis Testing
    • Article: Descriptive vs. Inferential Statistics
  • Hypothesis testing: Testing a Sample Statistic: Learn about hypothesis testing and implement binomial and one-sample t-tests in Python.
    • Article: The Central Limit Theorem
    • Article: Introduction to Hypothesis Testing (Simulating a One-Sample T-Test)
    • Lesson: One-Sample T-Tests in SciPy
    • Lesson: Simulating a Binomial Test
    • ExternalResource: Scipy Statistical Functions | Python
    • Lesson: Significance Thresholds
    • Quiz: Hypothesis testing for a Sample Statistic
    • Project: Heart Disease Research Part I
  • Hypothesis Testing: Testing an Association: Learn about hypothesis tests that can be used to evaluate whether there is an association between two variables.
    • Lesson: Hypothesis Testing: Associations
    • Quiz: Hypothesis Testing: Associations
    • Project: Heart Disease Research Part II
  • Experimental Design: Learn to design an experiment to make a decision using a hypothesis test.
    • Article: How to Choose a Hypothesis Test
    • Lesson: A/B Testing: Sample Size Calculators
    • Lesson: Sample Size Determination with Simulation
    • Quiz: Sample Size Determination
    • Project: A/B Testing at Nosh Mish Mosh
  • Hypothesis Testing Projects: Practice your hypothesis testing skills with some additional projects!
    • Project: Familiar: A Study In Data Analysis
    • Project: FetchMaker
    • Project: Analyzing Farmburg's A/B Test

Taught by

Kenny Lin

Reviews

4.6 rating at Codecademy based on 22 ratings

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