Statistics for Data Science with Python

Data-driven decisions require more than just writing code; they require a solid grasp of the underlying mathematics and logic. This comprehensive text-based course bridges the gap between theoretical statistics and practical Python programming, helping you turn raw numbers into actionable insights. You will transition from understanding basic data distributions to confidently performing complex statistical tests and modeling. What you'll learn: - Understand foundational statistical concepts including descriptive statistics, probability distributions, and the central limit theorem - Apply hypothesis testing, t-tests, and ANOVA to validate assumptions on real-world datasets - Implement linear and logistic regression models using modern Python libraries like pandas, statsmodels, and scipy - Practice data cleaning and exploratory data analysis (EDA) workflows to prepare messy datasets for statistical modeling - Leverage modern AI prompting techniques to explain complex statistical outputs and accelerate your analysis workflows - Conduct A/B testing scenarios to evaluate product changes and business decisions with statistical significance This course begins with core definitions, terminology, and probability concepts before guiding you step-by-step through practical Python implementations and data exercises. It is designed specifically for beginners with no prior statistics background, though a basic familiarity with Python syntax is helpful. Start reading today to build a strong, math-backed foundation for your data science journey.