JF
Department: Data Science School
Office location and address
31 Bonnycastle Dr
Charlottesville,
Virginia
22903
Publications
Sponsored Awards
Expeditions: Collaborative Research: Global Pervasive Computational Epidemiology
Source: U.S. National Science Foundation (NSF)
November 01, 2021 – April 30, 2025
Courses
Credits: 1–3
In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty. The study is often either a thorough analysis of an abstract computer science problem or the design, implementation, and analysis of a computer system (software or hardware). Prerequisite: Instructor permission.
Credits: 3
Required for Distinguished Majors completing the Bachelor of Arts degree in the College of Arts and Sciences. An introduction to computer science research and the writing of a Distinguished Majors thesis. Prerequisites: CS 2150 or CS 2501 topic DSA2 with a grade of C- or higher, and BSCS major
Credits: 3
The objective of this course is to introduce basic data analysis techniques including data analysis at scale, in the context of real-world domains such as bioinformatics, public health, marketing, security, etc. For the purpose of facilitating data manipulation and analysis, students will be introduced to essential programming techniques in Python, an increasingly prominent language for data science and "big data" manipulation. Prerequisite: CS 1110, Math 1310 or APMA 1110, Math 3351 or APMA 3080, Math 3100, APMA 3010 or APMA 3110
Credits: 3
An introduction to essential programming concepts, structures, and techniques. Students will gain confidence in not only reading code, but learning what it means to write good quality code. Additionally, essential and complementary topics are taught, such as testing and debugging, exception handling, and an introduction to visualization. This course is project based, consisting of a semester project and final project presentations.
Credits: 1
This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of data science.
Credits: 1–12
Graduate-level independent study conducted under the supervision of a specific instructor(s)
Credits: 1–12
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
Credits: 1–12
For master's students who are teaching assistants.
Credits: 1–12
Formal record of student commitment to thesis research for the Master of Science degree under the guidance of a faculty advisor. May be repeated as necessary.