Introductory Statistics with Python

Data-driven decision-making begins with understanding foundational statistics and knowing how to compute metrics using code. This text-based course offers a structured approach to interpreting data, evaluating distributions, and drawing reliable conclusions with Python. You will build a dependable analytical toolkit by working through clear explanations and practical code examples. By the end of the course, you will be able to summarize complex datasets, assess probability distributions, and conduct foundational hypothesis tests with confidence. What you'll learn: - Understand essential statistical concepts including mean, median, standard deviation, and variance - Calculate descriptive statistics and summary metrics using modern Python data libraries - Analyze discrete and continuous probability distributions to interpret data behavior - Apply hypothesis testing and confidence intervals to validate analytical findings - Evaluate relationships between variables using correlation and basic linear regression - Structure clean data exploration workflows using modern Python practices The course starts with core terminology and mathematical definitions, then walks through structured Python implementations and exploratory data scenarios. This course is created for beginners, career-switchers, and aspiring data analysts who want to build statistical fluency without needing advanced mathematics prerequisites. Begin your reading to master the core statistics that power real-world data analysis.