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Coursework

Coursework

Faculty advisors will help students determine their program of study. Courses starred with an asterisk * are currently unavailable to distance learning students.

CORE COURSES (18 credits)
Students are required to taken one course from each of the six categories below. The specific core courses taken will be determined in consultation with the student's faculty advisor.
Data Processing Course (3 credits) — one of:
CS 525 ADVANCED DATABASE ORGANIZATION
CS 554 DATA-INTENSIVE COMPUTING
CSP 595 BIG DATA TECHNOLOGIES
Statistics Course (3 credits) — one of:
MATH 563 MATHEMATICAL STATISTICS*
MATH 564 APPLIED STATISTICS
Machine Learning Course (3 credits) — one of:
CS 584 MACHINE LEARNING
MATH 569 STATISTICAL LEARNING
Working with Data Course (3 credits):
CSP/MATH 571 DATA PREPARATION AND ANALYSIS
Project Management Course (3 credits):
SCI 511 PROJECT MANAGEMENT
Communication Course (3 credits):
SCI 522 PUBLIC ENGAGEMENT FOR SCIENTISTS

ELECTIVE COURSES (9 credits)

Students may choose 3 courses from the list below. These electives must be approved by the student’s faculty advisor. 

Computation Fundamentals
CS 425 DATABASE ORGANIZATION
CS 430 INTRODUCTION TO ALGORITHMS
CS 450 OPERATING SYSTEMS
CS 525 ADVANCED DATABASE ORGANIZATION
CS 535 DESIGN AND ANALYSIS OF ALGORITHMS
CS 546 PARALLEL AND DISTRIBUTED PROCESSING
CS 553 CLOUD COMPUTING
CS 554 DATA-INTENSIVE COMPUTING
CS 589 SOFTWARE TESTING AND ANALYSIS
Computer Science Applications
CS 422 DATA MINING
CS 512 TOPICS IN COMPUTER VISION
CS 513 GEOSPATIAL VISION AND VISUALIZATION*
CS 522 ADVANCED DATA MINING
CS 529 INFORMATION RETRIEVAL
CS 556 CYBER-PHYSICAL SYSTEMS: LANGUAGES AND SYSTEMS
CS 557 CYBER-PHYSICAL SYSTEMS: NETWORKING AND ALGORITHMS
CS 579 ONLINE SOCIAL NETWORK ANALYSIS
CS 583 PROBABILISTIC GRAPHICAL MODELS
CS 584 MACHINE LEARNING
CS 585 NATURAL LANGUAGE PROCESSING
Mathematics, Probability and Statistics
MATH 454 GRAPH THEORY AND APPLICATIONS*
MATH 486 MATHEMATICAL MODELING I*
MATH 532 LINEAR ALGEBRA*
MATH 540 PROBABILITY*
MATH 542 STOCHASTIC PROCESSES*
MATH 553 DISCRETE APPLIED MATHEMATICS I*
MATH 554 DISCRETE APPLIED MATHEMATICS II*
MATH 565 MONTE CARLO METHODS
MATH 567 DESIGN OF EXPERIMENTS*
MATH 569 STATISTICAL LEARNING
MATH 574 BAYESIAN COMPUTATIONAL STATISTICS
Mathematical and Scientific Computing
MATH 577 COMPUTATIONAL MATHEMATICS I*
MATH 578 COMPUTATIONAL MATHEMATICS II*
MATH 590 MESHFREE METHODS*
BIOL 550 BIOINFORMATICS AND BIOTECHNOLOGY*
PHYS 440 COMPUTATIONAL PHYSICS*

Required Capstone Course (6 credits)
CSP/MATH 572 PRACTICUM
Required Data Science Seminar (0 credits)
CSP/MATH 570 DATA SCIENCE SEMINAR

Other relevant 500-level coursework may be taken for elective credit, with approval of the program director.

Students must take a total of at least 9 credits of MATH coursework and at least 9 credits CS/CSP coursework to graduate, not including the capstone practicum course. Prerequisite courses MATH 474, CS 201, and CS 401, if taken, will not count towards degree fulfillment.

The preferred course of study for the Master of Data Science is in-person at Main Campus—this will provide students with the best overall educational experience. We also offer the program online for students that are working full-time in data-related fields, in the US or Canada. Direct coordination between the program and students’ employers is essential for student success. Overseas students are encouraged to take recommended prerequisite courses online, then come to Chicago for the main course of study.