Statistical Tests in Python: Mathematical Foundations and Practical Code

Navigating statistical decision-making requires both a clear theoretical grounding and practical computational skills. Understanding statistical tests allows data enthusiasts to draw accurate inferences from data rather than relying on guesswork. This text-based course guides you from fundamental probability and distribution concepts through the mechanics of hypothesis testing. You will explore the mathematical foundations behind standard tests and learn how to execute them cleanly in Python. What you'll learn: Understand key concepts including null hypotheses, significance levels, and p-values; Master parametric tests such as t-tests, ANOVA, and z-tests; Apply non-parametric alternatives including Mann-Whitney U and Chi-Square tests; Implement statistical workflows using Python libraries like SciPy and Statsmodels; Interpret test results accurately to make confident, data-driven decisions. The course starts with essential definitions and mathematical principles before walking through practical code examples and edge-case handling. Perfect for beginners in data analysis and programming with no prior statistical background required. Gain the knowledge you need to analyze data with statistical rigor.