Course Requirements
Introductory Courses
| Course | Title | Quarter Hours |
|---|---|---|
| IT 403 | STATISTICS AND DATA ANALYSIS | 4 |
| CSC 412 | TOOLS AND TECHNIQUES FOR COMPUTATIONAL ANALYSIS | 4 |
| CSC 401 | INTRODUCTION TO PROGRAMMING | 4 |
Foundation Courses
| Course | Title | Quarter Hours |
|---|---|---|
| DSC 441 | FUNDAMENTALS OF DATA SCIENCE | 4 |
| DSC 430 | PYTHON PROGRAMMING | 4 |
| DSC 465 | DATA VISUALIZATION | 4 |
| DSC 450 | DATABASE PROCESSING FOR LARGE-SCALE ANALYTICS | 4 |
| DSC 445 | MACHINE LEARNING I (FORMERLY DSC 540) | 4 |
| CSC 483 | APPLIED DEEP LEARNING | 4 |
| CSC 484 | ETHICS IN ARTIFICIAL INTELLIGENCE | 4 |
Advanced Courses1
| Course | Title | Quarter Hours |
|---|---|---|
| DSC 424 | ADVANCED MODELING AND ANALYSIS TECHNIQUES | 4 |
| MKT 555 | MARKETING MANAGEMENT | 4 |
| MKT 534 | ANALYTICAL TOOLS FOR MARKETERS | 4 |
- 1
Some advanced courses may not be offered online. Students may consult with their faculty advisor to determine suitable course substitutions.
Elective Courses
Students must take four (4) credit hours of graduate-level elective courses. Elective courses must be selected from the following list:
| Course | Title | Quarter Hours |
|---|---|---|
| Select four (4) credit hours from the following: | 4 | |
| Advanced Data Analysis and Algorithms | ||
| TIME SERIES ANALYSIS AND FORECASTING | ||
| PROGRAMMING INTERACTIVE DATA VISUALIZATION FOR THE WEB | ||
| MONTE CARLO ALGORITHMS | ||
| TOPICS IN COMPUTER SCIENCE | ||
| PROBABILITY AND STATISTICS I | ||
| GENERALIZED LINEAR MODELS | ||
| BAYESIAN STATISTICS | ||
| ADVANCED BIOSTATISTICS | ||
| SURVIVAL ANALYSIS | ||
| OPERATIONS RESEARCH: OPTIMIZATION THEORY | ||
| Visualization and Image Analysis | ||
| INTRODUCTION TO IMAGE PROCESSING | ||
| APPLIED IMAGE ANALYSIS | ||
| COMPUTER VISION | ||
| SPATIAL DATABASES & GEOGRAPHIC INFORMATION SYSTEMS | ||
| GEOGRAPHIC INFORMATION SYSTEMS (GIS) FOR COMMUNITY DEVELOPMENT | ||
| GIS FOR SUSTAINABLE COMMUNITIES | ||
| INFORMATION VISUALIZATION AND INFOGRAPHICS FOR USER EXPERIENCE | ||
| Machine Learning and AI | ||
| RECOMMENDER SYSTEMS | ||
| ADVANCED DEEP LEARNING | ||
| ARTIFICIAL INTELLIGENCE II | ||
| NATURAL LANGUAGE PROCESSING | ||
| TOPICS IN ARTIFICIAL INTELLIGENCE | ||
| PROGRAMMING MACHINE LEARNING APPLICATIONS | ||
| MACHINE LEARNING II | ||
| MACHINE LEARNING ENGINEERING FOR PRODUCTION (MLOPS) | ||
| Databases and Data Management | ||
| DATABASE PROGRAMMING | ||
| MINING BIG DATA | ||
| INTELLIGENT INFORMATION RETRIEVAL | ||
| WEB DATA MINING | ||
| DATA WAREHOUSING | ||
| ENTERPRISE DATA MANAGEMENT | ||
| Applications | ||
| HEALTH DATA SCIENCE | ||
| TOPICS IN DATA ANALYSIS | ||
| SOCIAL NETWORK ANALYSIS | ||
| INFORMATION TECHNOLOGY CONSULTING | ||
| BUSINESS INTELLIGENCE AND ANALYTICS SYSTEMS | ||
| SPECIAL TOPICS | ||
| DIGITAL MARKETING ANALYTICS & PLANNING | ||
| DIGITAL BUSINESS STRATEGY | ||
| HEALTH SECTOR MANAGEMENT | ||
| SPECIAL TOPICS | ||
CMNS 549 | ||
Capstone Options
Four (4) credit hours are required for the capstone requirement. Students have the option of completing a real-world Data Science Project, or completing the Data Science Capstone course, or participating in a Data Science Internship or completing a Master's Thesis to fulfill their Capstone requirement.
- Data Analytics Project
- The real data science project is for students who are interested in working in a small team on a research project under the supervision of a CDM faculty. Students who are interested in proposing their own data analytics project are encouraged to contact a CDM faculty member teaching data science courses as soon as possible. Students must enroll in CSC 695 for a total of 4 credit hours taken in two consecutive quarters (2 credit hours for 2 quarters) to satisfy the capstone requirement. The faculty who supervises the project will initiate enrollment in the CSC 695 course.
- Data Science Capstone course
- DSC 672 course offers the opportunity of working on an analytics project in a more structured class format. Students enrolled in the courses will be working in teams on a data analytics project under the supervision of the course instructor.
- Data Science Internship
- An internship offers students the opportunity to integrate their academic experience with on-the-job training in an analytics related field. Students must enroll in CSC 697 for 4 credit hours to satisfy the practicum requirement. These are the steps:
- Secure an internship with focus in analytics.
- International Students must obtain the appropriate practical training form and meet with an advisor in the CDM Academic Center for approval. (https://offices.depaul.edu/global-engagement/student-resources/student-services/Pages/Forms.aspx)
- Login to MyCDM and click the “MyInternships” link on the left to start the course enrollment process.
- An internship offers students the opportunity to integrate their academic experience with on-the-job training in an analytics related field. Students must enroll in CSC 697 for 4 credit hours to satisfy the practicum requirement. These are the steps:
- Master's Thesis
- A student who has made an original contribution to the area (typically, through work done by CSC 695 may choose to complete a Master's Thesis. The student and the student's research advisor should form a Master's Thesis Committee of 3 faculty. The student will need to submit to the committee a thesis detailing the results of the research project. After a public defense, the committee will decide whether to accept the thesis. In that case, the student will be allowed to register for the 0 credit hour course CSC 698 and the transcript will show the thesis title as the course topic.