Data Anal. and Viz. in Python

1 Credit · Level: Graduate

This introductory course provides students with the foundational skills needed to analyze and visualize data using Python. Designed for beginners, the course focuses on developing a solid understanding of Python programming principles while applying them to real-world data analysis tasks. Students will learn how to read, understand, and write Python code, and explore essential libraries such as scikit-learn, pandas, NumPy, Matplotlib, and Seaborn for data manipulation and visualization. In addition to traditional programming instruction, the course introduces the use of artificial intelligence tools to assist in code generation and optimization. Students will learn how to assess AI-produced code critically, identify potential errors or hallucinations, and ensure the reliability of their analyses. By the end of the course, participants will be able to analyze and visualize clinical, biomedical, or publicly available datasets to generate meaningful insights. Students will have a final project on real data to demonstrate their analysis and visualization skills. Classes will be a mixture of lectures, demonstrations, and practical (hands-on) exercises. All assignments will be administered through Canvas, and due dates can be found on the Syllabus page in Canvas.