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.  

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 Coursework8
Complete two additional courses from the approved list.
Total Hours16

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.