Fundamentals of Data Science II

This course builds on CSCI E-101 , giving students a solid foundation for advanced data modeling, machine learning, and artificial intelligence (AI).

The focus of this course is data exploration and inference methods for understanding and interpreting complex relationships in modern datasets.

Datasets are becoming more complex and data analysis tools and methods are evolving rapidly with the introduction of generative AI.

As a result there is an increasing need for understanding and interpretation of complex results in order to make confident inferences.

The focus of this course is on twenty-first century methods for exploratory data analysis (EDA), inference and modeling arising from the analysis of increasingly complex datasets encountered in today's world.

Specific areas explored in this course include large-scale computational statistical inference for exploring and understanding complex data; graphical methods for exploring complex data and presenting results; modern resampling methods and Bayesian models for inference and exploration; and properties and models for analysis of complex time series.

The course includes a combination of theory and hands-on experience using Python programming tools.

The focus is on foundational computational statistical algorithms, statistical inference methods, and effective visualization and exploration methods.

The hands-on component of the course uses the Python packages, NumPy, Pandas, Seaborn, Statsmodels, and PyMC3, along with selected other open-source packages.

An independent project is required for all students registering for graduate credit.