To earn an MCDS degree, you must pass courses in the core curriculum, the MCDS seminar, a concentration area and electives. You must also complete a capstone project in which you work on a research project at CMU or on an industry-sponsored project. In total, you will complete 144 eligible units of study, including eight 12-unit courses, two 12-unit seminar courses and one 24-unit capstone course. You must take a certain number of core courses depending on your chosen area of concentration. The remainder of the 12-unit courses with course numbers 600 or greater can be electives chosen from the SCS course catalog. Any additional non-prerequisite units taken beyond the 144 units are also considered electives.
Here's a detailed breakdown of the curriculum.
Historically, students typically need a refresher on basic computer science systems before beginning graduate work at CMU. You must earn a B- or better in the undergraduate course 15-513 Introduction to Computer Systems (6 units), typically in the summer before your program commences. (This course is the distance education version of 15-213 Introduction to Computer Systems.) Failure to pass the course means that you have to take 15-213 during either the fall or spring semester, and the units will not count toward your 144 eligible units of study.
Core Curriculum and Area of Concentration
During your first semester the MCDS program, you will complete a required set of four (4) core courses: Cloud Computing, Machine Learning, Interactive Data Science and Data Science Seminar. By the end of your first semester, you must choose at least one (1) area of concentration— Systems, Analytics, or Human-Centered Data Science — which governs the additional core courses you will take beyond your first semester. To maximize your chance of success in the program, you should select a concentration area for which you're well-prepared, based on educational background, work experience and the areas described in your Statement of Purpose. You should carefully consider your choice of concentration area before you apply. You are strongly encouraged to review the detailed curriculum requirements for each area of concentration, in order to determine the best fit given your preparation and background.
Detailed Course Requirements
All students complete the Common Core in their first semester, and satisfy at least one Area of Concentration in subsequent semesters, according to the requirements listed below.
The following four (4) core courses are to be completed by all students during their first semester:
- 15-619 Cloud Computing
- 10-601 Machine Learning
- 05-839 Interactive Data Science
- 11-631 Data Science Seminar
To satisfy the Systems concentration, pick three Systems project courses:
- 15-605 Operating Systems Implementation
- 15-618 Parallel Computer Architecture & Programming
- 15-640 Distributed Systems
- 15-645 Database Systems
- 15-719 Advanced Cloud Computing
- 15-721 Advanced Databases
- 15-746 Storage Systems
To satisfy the Analytics concentration, pick three Analytics courses according to the guidelines given below.
Choose one course in Machine Learning/Statistics:
- 11-641 Machine Learning for Text Mining
- 11-661 Language and Statistics
- 11-777 Advanced Multi-modal Machine Learning
- 10-701 Advanced Machine Learning
- 10-605 Machine Learning with Big Data Sets
- 11-785 Introduction to Deep Learning
Choose one course in Software Systems:
- 11-791 Design and Engineering of Intelligent Info Systems
- 11-792 Information Systems Project
- 11-642 Search Engines
Choose one course with a focus on Big Data:
- 15-826 Multimedia Databases and Data Mining
- 10-605 Machine Learning with Big Data Sets
- 11-676 Big Data Analytics
- 11-775 Large-Scale Multi-media Analysis
Human-Centered Data Science (HCDS) Concentration
To satisfy the HCDS concentration, pick three HCDS courses according to the guidelines given below.
Choose one course in Behavioral Research Methods:
- 05-816 Applied Research Methods
- 94-834 Applied Econometrics I & II
Choose two courses in HCI Methods:
- 05-821 Social Web
- 05-823 E-Learning Design Principles and Methods
- 05-840 Tools for Online Learning
- 05-833 Applied Gadgets, Sensors and Activity Recognition in HCI
- 05-836 Usable Privacy and Security
- 05-872 Rapid Prototyping of Computer Systems
- 05-899 Crowd Programming
- 05-899 Special Topics in HCI: Sensemaking
- 05-899 Human Multimodal Communication and Probabilistic Learning
- 05-899 Design of Large-scale Peer Learning Systems
- 05-899 Learning with Peers at Massive Scale
- 05-899 Mobile Health
- 05-899 Psychological Foundations for Designing Impact in HCI
Every student must complete a capstone project that integrates classroom experience with hands-on research. Working alone or as part of a team, you'll solve a research problem with either a Carnegie Mellon or industry partner.
Every student is required to complete an industry internship or adequate practical training. This typicall happens in the summer between the first fall and spring semesters.
You can take three elective courses. The electives should be any graduate-level, 12-unit course in the School of Computer Science.
Working Directly With Faculty
Some students want to explore more research-oriented studies conducted directly with a faculty member. To perform this kind of work, your background and the faculty member's interest must be closely aligned. You will take an independent study course as an elective and work with the faculty member. If you're interested in this option, please contact the director.