Hypothesis Testing for Data Science

Hypotheses (explanations for things that are yet to be proved) are an important facet of data science, as they help to drive innovation and progress. This course will show you how to strengthen your hypotheses by supporting your claims with statistical evidence. From a foundational understanding of the concepts underlying hypothesis testing, to working through lots of different, practical examples, and even coding your own testing framework from the ground up, you will have everything that you need to create bullet-proof hypotheses.

In this course, you will learn:

  • Random Variables
  • Probability Distributions (including Gaussian/Normal Distributions)
  • z-tests
  • t-tests

A solid understanding of probability theory, analyzing data with Pandas, and visualizing data. We recommend you complete Probability Foundations for Data Science, Data Analysis with Pandas, and The Complete Python Data Visualization Course before taking this course.

Tools and Frameworks

Python 3.7, Anaconda 5.3, SciPy 1.1, Matplotlib 3.0

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