Introduction to Data Science

LYOFN26 (Available for all periods: 10:00-11:00)

This course covers the fundamental concepts of data cleaning, organization, processing, and analysis. Students will work on diverse data analysis problems encountered in data-intensive applications. The course requires students to engage in numerous in-class programming exercises alongside practical case studies. The first half of the course introduces Data Science through essential Python libraries, including Matplotlib, NumPy, Pandas, and Seaborn. The second half focuses on core Machine Learning approaches, starting with the basics and progressing through different learning paradigms, regression/classification problems, evaluation metrics, generalization, and overfitting.

Students are expected to have a foundational knowledge of Python (variables, arithmetic operations, control flow, loops, data structures, and functions) prior to enrollment. These fundamental Python topics will not be covered in class; prior proficiency is assumed.

NOTE: Students must have their own computers to participate in this course.