University of Illinois Urbana-Champaign University of Illinois Urbana-Champaign 10 courses 16 total credits
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) β 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 studentβs 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...
| Total Hours | 16 credits |
Core Coursework
Select at least one course from two of the three groups below. Mathematical Foundations (MF) &Statistical Thinking (ST) Mathematical Foundations (MF) & Computational Thinking (CT) Data Description and Curation (DX) & Data Modeling (DX)
| Code | Course Title | Credits |
|---|---|---|
| STAT 425 | Statistical Modeling I | 3-4 |
| STAT 432 | Basics of Statistical Learning | 3-4 |
| CSE 448 | Advanced Data Analysis | 4 |
| CS 441 | Applied Machine Learning | 3-4 |
| CS 446 | Machine Learning | 3-4 |
| CS 450 | Numerical Analysis | 3-4 |
| CS 484 | Parallel Programming | 3-4 |
| CSE 428 | Statistical Computing | 3-4 |
| STAT 480 | Big Data Analytics | 3-4 |
| CS 412 | Introduction to Data Mining | 3-4 |
| Total Core Coursework | 8 credits | |
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