The course teaches students how to think like a statistician, a crucial skill for making data-based decisions and understanding advanced AI models. It covers probability theory, descriptive and inferential statistics, programming concepts, and algorithm development using Python. Students will gain hands-on experience in data pre-processing, and designing, evaluating and implementing predictive models. The course covers widely used statistical modeling techniques such as Linear Regression, Naïve Bayes Classifier, and Logistic Regression. By the end of the course, students will be able to identify and apply appropriate techniques, perform analyses, and present their findings professionally.