Subject Code: DATA • Section 9.5
Undergraduate courses offered by the faculty in Data Science, supporting major curricula, academic minors, and the Alkimi Core.
Catalog Documentation
Courses in Data Science are designed around rigorous empirical, theoretical, and applied principles. Coursework builds systematically from 100-level introductory surveys through 400-level advanced capstones and research seminars.
Prerequisites must be completed with a grade of C- or higher (unless specified otherwise by the academic department). Students who have not satisfied course prerequisites may only enroll with explicit written permission of the instructor and department chair.
Catalog Citation
Official descriptions, credit distributions, and prerequisites authorized by the Faculty Senate.
Subject Summary
Complete Listings
Exploratory data analysis, data wrangling with pandas/R, data visualization, web scraping, and foundational predictive modeling applied to real-world domain problems.
Supervised learning models, classification and regression trees, random forests, support vector machines, clustering algorithms, and model validation techniques.
Distributed storage, map-reduce paradigms, Apache Spark, stream processing, cloud data warehouses, and scalable pipeline architectures for massive datasets.
Deep learning architectures, backpropagation algorithms, convolutional neural networks, recurrent neural networks, transformers, and regularization in PyTorch.
Comprehensive team project solving an authentic enterprise or scientific predictive modeling problem, concluding with a production pipeline and public presentation.
