| Subjects | Statistics | Algorithmic Foundation | Tools/Syst for Massive Data | Knowledge Discovery | Visualization |
|---|---|---|---|---|---|
| Required Courses: 5 Total | Stat581: Probability & Statistics for Data Science | CS512: Data Structures & Algorithms | CS537: Massive Data Storage & Retrieval Tools (taught by MSDS faculty) | CS542: Massive Data Mining and Learning (taught by MSDS) | CS524: Data Interaction and Visual Analytics (taught by MSDS faculty) |
| Required Capstone I CS554 | First semester of the 2nd year: Hands-on Experience; Project and Industry Exposure (taught by MSCS faculty) |
||||
| Elective Courses: (at most 2 per area) | Stat 697: Data Wrangling and Husbandry | CS521: LP/Optimization | CS539: Databases | CS520: AI | CS534: Computer Vision |
| Stat596: Intermediate Statistical Methods -- Regression and Time Series | CS513/CS514: Advanced Algorithms | CS546/CS547: Security/Privacy | CS535/CS536: Machine Learning | CS523: Computer Graphics | |
| Elective Capstone II CS555 or Independent Studies |
Second semester of the 2nd year: Hands-on Experience Project Prerequisite: Excellent performance in Capstone I (taught by MSCS faculty) or up to three credits of Independent Studies (604, 605, 606). |
||||
The six foundational classes expose students to the identification of questions whose answers can be aided by data retrieval, data cleaning and data modeling tools, plus specialized algorithmic and statistical processing, machine learning, pattern recognition and interactive visualization tools. A faculty supervised CapStone class is dedicated to building prototype systems where students exercise the skill set acquired in the other foundational classes.
The six remaining elective courses offer students the opportunity of further specializations in Statistics, Algorithms, Optimization, Machine Learning, Data Privacy/Security, Computer Graphics and Vision. An elective second Capstone project can be used, at the student's discretion, to compete in a Master wide context in Data Science.
