This course will equip students with technical foundations in data science and AI while focusing on fairness, bias mitigation, and the responsible design and deployment of AI systems. The topics include basics of data science, Python for data science, essential Python packages for data science (Pandas, Numpy), data collection, data cleaning, exploratory data analysis, data visualization, data aggregation and manipulation, statistical analysis and regression, and three special data topics (geospatial data, network data, and text data), and GenAI's role in data science.
Weekly schedules for EST 389, spring, 2026
What is data science: slides
Python basics for data work: slides | Code demos
Data collection: slides
Essential data science packages in python: slides | Code demos
Data cleaning: slides | Code demos
Exploratory data analysis: slides | Code demos
Data visualization: slides | Code demos
Core data manipulation: slides | Code demos
Advanced data operations: slides | Code demos
Statistical analysis: slides | Code demos
Special data topic - geospatial data analysis: slides | Code demos
Special data topic - network data analysis: slides | Code demos
Generative AI and data science: slides | Code demos