Hypothesis Testing for Data Science
Learn to code frameworks that will support your hypotheses with statistical analysis.
- 2.8 Hours of Video
- Certificate of Completion
- Source Code
- Closed Captions
- On-demand, 24/7 access
- 2.8 hours of video
- Certificate of completion
- Source code and PDF notes
- Closed captions
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)
Frameworks and tools covered: Python 3.7, Anaconda 5.3, SciPy 1.1, Matplotlib 3.0
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- A solid understanding of probability theory, analyzing data with Pandas, and in visualizing data is required to complete this course. If you have not already done so, we highly recommend that you first complete Probability Foundations for Data Science, Data Analysis with Pandas , and The Complete Python Data Visualization Course before taking this course.
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