Applied Statistics and A/B Testing in Python

Many self-taught data professionals struggle with the underlying mathematical rigor required for confident analysis. This course bridges that knowledge gap by transforming you from a code-first learner into a statistically sound data practitioner. You will learn to design experiments, validate hypotheses, and draw robust conclusions using Python. What you will learn: • Understand foundational statistical terminology, probability distributions, and core concepts. • Design and evaluate A/B tests to measure the real-world impact of business decisions. • Apply statistical inference and hypothesis testing to practical datasets. • Calculate sample sizes, statistical power, and confidence intervals to ensure reliable results. • Write clean, reproducible Python code using modern dataframe libraries and virtual environments. • Recognize and avoid common analytical pitfalls and biases in experimental design. The curriculum begins with essential terminology and foundational probability concepts before moving into practical inference and experimental design. You will read clear explanations and work through written Python code snippets that reinforce your understanding of how statistics apply to real data. Designed specifically for beginners and self-taught analysts, this course requires no prior advanced math background. Start building your statistical intuition today and take the guesswork out of your data analysis.