Statistical Modeling

Catalog description

Prerequisites: DDS 8501 with a minimum grade of C. Sufficient knowledge of EDA Introducing statistical techniques is essential for extracting meaningful insights from data focusing on projects and research of Data Science. Through a comprehensive eight-week journey, students will explore topics such as normal distribution, hypothesis testing, power of test, type I and type II errors, sampling distributions, bootstrapping methods, diagnostic tools, validation techniques, and more. The course emphasizes practical applications, equipping learners with the skills to make data-driven decisions and extract hidden patterns from datasets. By mastering these inferential statistics techniques, students will be well-prepared to tackle complex real-world problems and enhance their expertise in the field of Data Science.