Statistical Modeling and Diagnostics

О курсе

In this course, you will learn how to analyze relationships between variables, detect predictive signals, validate statistical model assumptions, and apply transformations to improve model performance. You will construct and interpret linear and logistic regression models, explore methods for controlling bias in observational data, and learn how to monitor deployed models for drift or degradation. These skills form the foundation of rigorous statistical thinking and are essential for anyone working with real-world data. Learners benefit from a uniquely multidisciplinary experience shaped by experts from Edureka, Google, Illinois Tech, Genentech, and Microsoft. This diversity allows you to see statistical modeling from multiple professional perspectives—from academic theory and diagnostic rigor to industry applications in causal inference and cloud-based model management. By engaging with hands-on exercises and practical examples, you will strengthen your ability to build reliable, interpretable, and production-ready models. Whether you are preparing for more advanced machine learning coursework or looking to deepen your statistical intuition, this course will equip you with the tools, reasoning, and confidence needed to analyze data effectively and support sound decision-making.

Навыки, которые вы освоите

Учитесь у экспертов

Professionals from the Industry

Преподаватель курса