This course provides a foundational introduction to data-driven problem-solving. Students will learn to read data, produce insightful graphs, perform hypothesis testing, and apply Bayesian statistics – all with hands-on programming experience. Delves into the core principles of pattern recognition, examining techniques for identifying recurring structures and relationships within data. This will form a crucial foundation for understanding more advanced subjects such as machine learning, where these techniques are extensively applied. Specifically, the course will explore concepts like clustering, dimensionality reduction, and model evaluation, illustrating how these approaches build upon the analytical foundations developed in this course.