Statistical Inference and Model Validation
About This Course
This three-day module offers hands-on practice in statistical inference and model validation using Python. It emphasises on key concepts such as p-value, significance level, test statistics, critical value, and type I and II errors. Participants will learn when and how to perform parametric tests as well as non-parametric tests on data, as well as how to perform model validation to assess the appropriateness of a selected statistical model. It concludes with a comprehensive project that demonstrates the application of learned skills in a real-world scenario.
What You'll Learn
• Implement parametric testing methods such as the Z-test, T-test (Student's t-test) and ANOVA(Analysis of Variance) using Python.
• Implement non-parametric tests such as the Wilcoxon Signed Rank and Chi-Square using Python.
• Assess data normality using graphical and numerical approaches.
• Validate the appropriateness of a selected statistical model.
Entry Requirements
• Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.
• Participants should preferably have basic knowledge of statistics (descriptive and inferential statistics).
• Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
• Participants are encouraged to complete the Foundation of Data Science before enrolling in this course.
• Participants are required to pass a pre-course assessment to ensure participants have the requisite knowledge of Python programming. This assessment can be waived if participants have completed both Fundamentals in Python (Basic) and Fundamentals in Python (Intermediate).