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.

Preparation Prerequisite
All students entering the MCDS program are encouraged to complete 11-637 Foundations of Computational Data Science, during the Summer session prior to arriving in Pittsburgh. You must earn a B- or better in this course (12 units). Students who fail to pass the course during Summer must (re-)enroll in the class during Fall semester.

Core Curriculum and Area of Concentration
During your first two semesters in the MCDS program, you will complete a required set of five (5) core courses: Foundations of Computational Data Science, 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 two semesters, and satisfy at least one Area of Concentration in subsequent semesters, according to the requirements listed below. (See MCDS Course Map for an illustration.)

Common Core

The following five (5) core courses are to be completed by all students during their first two semesters:

  • 11-637 Foundations of Computational Data Science
  • 15-619 Cloud Computing
  • 10-601 Machine Learning
  • 05-839 Interactive Data Science
  • 11-631 Data Science Seminar

Systems Concentration

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
    • 15-641 Computer Networks
    • 15-645 Database Systems
    • 15-712 Advanced and Distributed Operating Systems
    • 15-719 Advanced Cloud Computing
    • 15-721 Advanced Databases
    • 15-746 Advanced Storage Systems
    • 15-821 Mobile and Pervasive Computing

    Analytics Concentration

    To satisfy the Analytics concentration, pick three Analytics courses according to the guidelines given below.

    Choose one course in Machine Learning/Statistics:

    • 10-617 Intermediate Deep Learning
    • 10-701 Introduction to Machine Learning (Ph.D.)
    • 10-703 Deep Reinforcement Learning & Control
    • 10-707 Topics in Deep Learning
    • 10-708 Probabilistic Graphical Models
    • 10-715 Advanced Introduction to Machine Learning
    • 10-716 Advanced ML: Theory & Methods
    • 10-718 Data Analysis
    • 10-725 Convex Optimization
    • 11-755 Machine Learning for Signal Processing
    • 11-761 Language and Statistics
    • 11-777 Multimodal Machine Learning
    • 11-785 Introduction to Deep Learning
    • 36-705 Intermediate Statistics
    • 36-708 Statistical Methods in Machine Learning

    Choose one course in Software Systems:

    • 11-642 Search Engines
    • 11-737 Multilingual NLP
    • 11-747 Neural Networks for NLP
    • 11-788 Computational Forensics & AI
    • 11-797 Question Answering
    • 11-830 Computational Ethics for NLP

    Choose one course with a focus on Big Data:

    • 10-605 Machine Learning with Large Data Sets
    • 10-745 Scalability in Machine Learning
    • 10-805 Machine Learning with Large Data Sets
    • 11-741 Machine Learning for Text Mining
    • 11-775 Large-Scale Multimedia Analysis
    • 11-777 Multimodal Machine Learning

    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:

    • 05-610 User Centered Research and Evaluation
    • 05-816 Applied Research Methods

    Choose two courses in HCI Methods:

    • 05-618 Human AI Interaction
    • 05-891 Designing Human Centered Systems
    • 05-813 Human Factors
    • 05-821 Social Web
    • 05-823 E-Learning Design Principles and Methods
    • 05-840 Tools for Online Learning
    • 05-833 Gadgets, Sensors & Activity Recognition
    • 05-836 Usable Privacy and Security
    • 05-838 The Role of Tech in 21st Century Learning
    • 05-872 Rapid Prototyping of Computer Systems
    • 05-899 Crowd Programming
    • 05-899 Learning Analytics and Educational DS
    • 05-899 HCI for Startups
    • 05-899 Accessibility
    • 05-899 Fairness, Accountability, Transparency & Ethics
    • 05-899 Persuasive Design
    • 05-899 Social Data Science
    • 05-899 Transformational Game Design Studio

    Capstone Project
    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.

    Internship
    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.

    Electives
    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.