Statistical Methods for Data Science

Statistical Methods for Data Science is a practical course that teaches the statistical tools behind strong analysis, clear inference, and smarter decisions. Designed for learners who want a data-driven foundation in inference, modeling, and decision-making with Professor Nathan Ward, it helps you turn raw data into reliable insights for Data Science work.

Build Practical Data Science Skills With Statistical Methods

• Learn the core statistical ideas that support confident Data Science analysis
• Understand how to work with data types, summaries, and visualizations effectively
• Apply probability, sampling, and estimation to real analytical problems
• Use hypothesis testing, regression, and model evaluation to support decisions

A practical, data-driven foundation in inference, modeling, and decision-making with Professor Nathan Ward.

This course begins with why statistics matters in Data Science and builds a strong foundation in variables, measurement, and data quality. You will learn how to describe data with center, spread, and shape, and how to use charts to reveal distributions and relationships. These early lessons help you see data more clearly and prepare you for deeper analytical work.

As the course progresses, you will study probability, common distributions, sampling, the central limit theorem, estimation, and confidence intervals. You will also explore hypothesis testing, p-values, errors, power, t-tests, proportion comparisons, correlation, and association. Each topic is designed to strengthen your ability to interpret evidence, measure uncertainty, and make informed conclusions from data.

You will then move into regression analysis, including simple and multiple regression, diagnostics, chi-square tests, bootstrap and permutation methods, cross-validation, and model evaluation. The course closes with guidance on communicating statistical results clearly and professionally. By the end, you will be more confident reading, analyzing, and explaining data, and you will be better prepared to apply Statistical Methods for Data Science in real-world settings.