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

Frameworks and tools covered: Python 3.7, Anaconda 5.3, SciPy 1.1, Matplotlib 3.0

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Our course instructors participate in elite developer programs and have been recognized for their demonstrated excellence in development and teaching.

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Interactive Lessons with Codemurai

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Available on iOS and Android, it’s full of interactive programming lessons and exercises so you can continue your learning on the go.

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