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Hypothesis Testing in Python
4.8(3,584)59k studentsINTERMEDIATEEnglish
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Overview
This intermediate Python course covers fundamental statistical tests to evaluate data-driven hypotheses with rigor. Using real-world datasets such as Stack Overflow user survey feedback and medical supply shipment data, you learn how and when to apply t-tests, proportion tests, chi-square tests, and non-parametric alternatives.
Who It Is For
Data analysts and Python developers seeking to make statistically sound inferences and validate assumptions across diverse datasets.
Key Takeaways
- Execute t-tests, proportion tests, and chi-square tests using Python.
- Understand underlying test assumptions and sample characteristics.
- Leverage non-parametric tests to analyze data when traditional assumptions fail.
About this course
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.
The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
Price
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