Hypothesis Testing and Statistical Inference with Python

How do you know if a pattern in your data is a genuine discovery or just a random coincidence? To make confident, data-backed decisions, you must understand the rules of statistical significance and hypothesis testing. This text-based course guides you from foundational probability concepts to running statistical tests in Python. You will learn how to formulate hypotheses, analyze categorical data, and draw reliable conclusions from real-world datasets using industry-standard libraries like SciPy and pandas. What you'll learn: • Understand foundational probability concepts, statistical distributions, and hypothesis formulation. • Calculate degrees of freedom and interpret p-values to determine statistical significance. • Perform single and multi-category chi-square tests to analyze categorical data. • Apply modern Python statistical libraries, including SciPy and pandas, to run hypothesis tests. • Analyze real-world text and tabular datasets to uncover hidden patterns and winning strategies. • Write clean, readable statistical code using modern Python data conventions. Starting with core terminology and the logic of statistical significance, you will gradually progress through practical testing scenarios. You will read clear explanations, study structured code examples, and practice your skills by analyzing real-world datasets. This course is designed for beginners who have a basic grasp of Python and want to build a solid foundation in data analysis and statistics, with no advanced mathematics background required. Start testing your data and making scientifically sound decisions today.