Statistics and A/B Testing for Data Science Interviews

Preparing for data analyst or machine learning interviews requires a solid grasp of statistics, but textbook formulas can feel disconnected from real-world hiring scenarios. This text-based course bridges the gap by explaining core statistical theory and its practical application in interview settings. You will build a strong intuitive understanding of probability, distributions, and statistical tests. By working through clear written explanations and conceptual exercises, you will learn how to design A/B tests, select the right statistical frameworks, and articulate your reasoning clearly to interviewers. What you'll learn: - Understand foundational probability concepts, random variables, and key distributions - Apply parametric and non-parametric statistical tests to compare data groups - Design rigorous A/B tests, including sample size estimation and power analysis - Analyze experimental results while controlling for multiple testing errors - Practice solving common statistical problems encountered in technical interviews - Explain complex statistical concepts in simple, business-oriented terms The course starts with fundamental definitions and probability basics before moving into hypothesis testing, modern experimental design, and common interview scenarios. You will progress through clear explanations and structured practice cases designed to reinforce your learning. This course is designed for aspiring data analysts, data scientists, and machine learning engineers preparing for technical interviews. No advanced mathematical background is required, as we build up from the basics. Start reading today to build your statistical confidence and ace your next technical interview.