Data Science
The data science master’s will provide you with the technical knowledge and advanced computational skills to meet emerging challenges in big data analytics. With on-campus and online classroom learning formats, you can launch a career in data science with extraordinary faculty members—anytime, anywhere. Our online learning program replicates the classroom experience with 100 percent access to software, faculty advisors, career services, and a large network of alumni. Discover how you can manage and analyze complex data, develop data science models to support decision making, and effectively communicate analytical results to a non-technical audience.
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Concentrations
Computational Methods
Computational Methods
The Computational Methods concentration is designed for data science master’s students who want to develop strong technical and programming skills to solve complex data problems. You’ll be trained to handle and analyze massive amounts of data and to use appropriate analytics and machine learning techniques to gain meaningful insights from data.
Health Care
Marketing
Hospitality
Careers
$103,930Average salary of a data scientist according to the US Bureau of Labor Statistics (May, 2020)
Our graduates work at Allstate, Amazon, BMO Harris Bank, Deloitte, Facebook, GE Capital, and IBM.
Curriculum Requirements
Introductory Courses
No Introductory Course may be substituted for any other course at any level.
Introductory courses may be waived for any of the following conditions based on faculty review:
- The student has the appropriate course work to satisfy an Introductory Course based on an official transcript review by faculty and successful grades, typically B or better.
- The student has appropriate and verified professional experience to satisfy an Introductory Course which is demonstrated through successful completion of a GAE exam.
- If a Graduate Assessment Examination (GAE) is available for the Introductory Courses, upon successfully completion of a GAE, a waiver will be issued.
- IT 403 Statistics and Data Analysis
- CSC 412 Tools and Techniques for Computational Analysis
- CSC 401 Introduction to Programming
Foundation Courses
- DSC 450 Database Processing for Large-Scale Analytics
- DSC 423 Data Analysis and Regression
- DSC 424 Advanced Data Analysis
- DSC 430 Python Programming
- DSC 441 Fundamentals of Data Science
Students must take 1 course in applied analytics chosen among:
Advanced Courses
- DSC 478 Programming Machine Learning Applications
- CSC 555 Mining Big Data
- DSC 540 Advanced Machine Learning
Students must take 1 course among the following:
- CSC 521 Monte Carlo Algorithms
- CSC 575 Intelligent Information Retrieval
- CSC 578 Neural Networks and Deep Learning
Elective Courses
Students must take 8 credit hours from graduate level elective courses in the areas of statistical modeling, data mining or database technologies. Students must choose electives from the following list of courses:
- DSC 425 Time Series Analysis and Forecasting
- DSC 433 Scripting for Data Analysis
- CSC 452 Database Programming
- DSC 465 Data Visualization
- DSC 478 Programming Machine Learning Applications
- CSC 481 Introduction to Image Processing
- CSC 482 Applied Image Analysis
- DSC 480 Social Network Analysis
- DSC 484 Web Data Mining
- CSC 521 Monte Carlo Algorithms
- CSC 528 Computer Vision
- DSC 510 Health Data Science
- DSC 540 Advanced Machine Learning
- CSC 543 Spatial Databases and Geographic Information Systems
- CMNS 549 Special Topics in Organizational Communication
- CSC 555 Mining Big Data
- CSC 575 Intelligent Information Retrieval
- CSC 576 Computational Advertising
- CSC 577 Recommender Systems
- CSC 578 Neural Networks and Deep Learning
- CSC 594 Topics in Artificial Intelligence
- CSC 598 Topics in Data Analysis
- GEO 441 Geographic Information Systems (Gis) for Community Development
- GEO 442 Geographical Information Systems (Gis) for Sustainable Urban Development
- GPH 565 Designing for Visualization
- HCI 512 Information Visualization and Infographics
- IPD 451 Big Data and NoSQL Program
- IS 549 Data Warehousing
- IS 550 Enterprise Data Management
- IS 574 Business Intelligence and Analytics Systems
- IS 478 Information Technology Consulting
- MGT 559 Health Sector Management
- MGT 798 Special Topics*
- MKT 555 Marketing Management
- MKT 530 Customer Relationship Management
- MKT 534 Analytical Tools for Marketers
- MKT 595 Digital Marketing Analytics and Planning
- MKT 570 Service Design and Patient Experience*
- MKT 798 Special Topics
*Course is not currently offered online.
NOTE: Student must take only sections of MGT 798 and MKT 798 with the topic specified above.
Capstone Options
Students have the option of completing a real world Data Analytics Project, or completing the Data Science capstone course, or participating in an Analytics Internship or completing a Master's Thesis to fulfill their Capstone requirement.
Data Analytics Project
Data Science Capstone course
Analytics Internship
Master's Thesis
Degree Requirements
Students in this degree program must meet the following requirements:
- Complete a minimum of 52 graduate credit hours in addition to any required introductory courses of the designated degree program.
- Complete all graduate courses and requirements listed in the designated degree program.
- Earn a grade of C- or better in all courses of the designated program.
- Maintain a cumulative GPA of 2.5 or higher.
- Students pursuing a second (or more) graduate degree may not double count or retake any course that applied toward the completion of a prior graduate degree. If a required course in the second degree was already completed and applied toward a previous degree, the student must meet with a faculty advisor to discuss a new course to be completed and substituted in the new degree. This rule also applies to cross-listed courses, which are considered to be the same course but offered under different subjects.
- Students pursuing a second master's degree must complete a minimum of 52 graduate credit hours beyond their first designated degree program in addition to any required introductory courses in their second designated degree program.
Students with a GPA of 3.9 or higher will graduate with distinction.
For DePaul's policy on repeat graduate courses and a complete list of academic policies see the DePaul Graduate Handbook in the Course Catalog.
