Introduction to Data Science

Catalog description

This course provides an introductory exploration of data science, integrating probability and statistics, programming, and machine learning to prepare students for more advanced coursework. Designed for those with little to no background in machine learning, it offers a practical, hands-on approach that combines statistical reasoning, computational thinking, and real-world applications. Unlike traditional machine learning courses that emphasize theoretical foundations, this course focuses on applied learning. Students will develop proficiency in Python and gain experience implementing data-driven models using NumPy, Pandas, and Scikit-learn. Topics will include fundamental probability and statistics concepts, core machine learning techniques, and essential programming skills for data analysis. By the end of the course, students will have a strong foundational understanding of data science and be well-prepared to pursue more advanced topics in the field.