Probability and Statistics for Data Science in Python

To make sense of complex data, you must understand the underlying mathematical principles that govern it. This text-based course bridges the gap between theoretical probability and hands-on data analysis using Python. You will transition from a beginner to a confident practitioner capable of exploring datasets, testing hypotheses, and making data-driven decisions using clean, modern Python code. What you'll learn: - Understand core probability concepts, including conditional probability, Bayes' theorem, and probability distributions. - Calculate descriptive statistics and summarize data characteristics using modern Python libraries. - Apply hypothesis testing and regression analysis to validate assumptions and discover relationships in data. - Implement random sampling, confidence intervals, and the Central Limit Theorem to make accurate population inferences. - Practice writing clean, reproducible statistical workflows using modern Python conventions and type hints. The course begins with key terminology, basic concepts, and foundational definitions of probability before moving into practical data manipulation. You will read comprehensive explanations, study structured code examples, and practice your skills through written exercises designed to reinforce your analytical thinking. This course is designed for aspiring data analysts, programmers, and beginners who want to build a solid mathematical foundation for data science without any prior statistical background. Start reading today to unlock the power of statistical thinking in your data science journey.