Data Science & Engineering Concentration
for the graduate concentration in Data Science & Engineering
program director: Luke Olson
overview of admissions & requirements: https://cse.illinois.edu/cse-educational-programs/graduate-concentration/
overview of grad college admissions & requirements: https://grad.illinois.edu/admissions/apply
program website: https://cse.illinois.edu
program website: https://cse.illinois.edu/cse-educational-programs/graduate-concentration/
program faculty: https://cse.illinois.edu/about-us/faculty-affiliates/
college website: https://grainger.illinois.edu/
contact: Bryan Wang
department office: 1205 W Clark St, Suite 2102, Urbana, IL 61801
phone: (217) 300-5696
email: cse@cse.illinois.edu
The Data Science & Engineering (DSE) Transcriptable Graduate Concentration is designed primarily for graduate students at the Ph.D. levels with an interest in data intensive computing. Data science plays a major role in many areas of computational science and engineering
(CSE) — the DSE Concentration is open to domain scientists working in this area. This concentration requires students to complete 16 graduate credit hours spanning data science, from topics in mathematical foundations (MF), computational thinking (CT), statistical thinking (ST), as well as data management, description, and modeling (DX). Courses taken toward this concentration will count towards the student’s graduate degree if permitted by the curriculum of their major, and the concentration will be listed on their transcript upon graduation.
To fulfill the requirements of the graduate concentration, students will take courses selected from an established list of core courses, along with a courses from a selection of elective courses that span a range of domain areas. Students may select any course in the list of electives, regardless of their enrolled degree program.
Additionally, understanding the ethical and societal implications of the application of data science is paramount, and CSE will integrate the latest topics to help educate future data scientists on appropriately developing and applying data science algorithms that impact society. To ensure that students in the Data Science & Engineering Graduate Concentration are exposed to current topics in this area and to highlight the how data science decisions can have real-world significance, CSE will (1) require that all DSE-seeking students attend at least one seminar on data science and social justice and (2) complete the self-paced Practical Data Ethics course developed by the UCSF Center for Applied Data Ethics. Students must affirm that they completed the course and will be required to report on their experience in order to receive the DSE Concentration. CSE will annually evaluate this requirement as additional on- and off-campus resources become available.
This graduate concentration is only available for students enrolled in these participating graduate degree programs:
Aerospace Engineering, PhD
Agricultural & Biological Engineering, PhD
Bioengineering, PhD
Civil Engineering, PhD
Computer Science, PhD
Electrical & Computer Engineering, PhD
Industrial Engineering, PhD
Materials Science & Engineering, PhD
Mechanical Engineering, PhD
Nuclear, Plasma, & Radiological Engineering, PhD
Physics, PhD
Statistics, PhD
Admission
For more information regarding the Data Science & Engineering (DSE) Graduate Concentration, visit the Computational Science and Engineering website, or contact the CSE Office at 217-333-3247 or cse@cse.illinois.edu.
for the graduate concentration in Data Science & Engineering
For more information regarding the Data Science & Engineering (DSE) Graduate Concentration, visit the Computational Science and Engineering website, or contact the CSE Office at 217-333-3247 or cse@cse.illinois.edu.
| Code | Title | Hours |
|---|---|---|
| Core Coursework | 8 | |
| Select at least one course from two of the three groups below. | ||
| Mathematical Foundations (MF) &Statistical Thinking (ST) | ||
| Statistical Modeling I | ||
| Basics of Statistical Learning | ||
| Advanced Data Analysis | ||
| Applied Machine Learning | ||
| Machine Learning | ||
| Mathematical Foundations (MF) & Computational Thinking (CT) | ||
| Numerical Analysis | ||
| Parallel Programming | ||
| Advanced Data Analysis | ||
| Data Description and Curation (DX) & Data Modeling (DX) | ||
| Data Science Foundations | ||
| Introduction to Data Mining | ||
| Elective Coursework | 8 | |
Complete two additional courses from the approved list. | ||
| Total Hours | 16 | |
Other Requirements
| At least 4 hours of coursework for the DSE concentration should be advanced (500-level courses) |
| For students enrolled in both the DSE concentration and the CSE concentration, at least 12 hours of coursework earned for the DSE concentration must be distinct from credit earned for the CSE concentration. |