Carnegie Mellon University

Master of Computational Data Science

The Master of Computational Data Science (MCDS) program focuses on engineering and deploying large-scale information systems, and includes concentrations in Systems, Analytics, and Human-Centered Data Science.

The MCDS degree focuses on engineering and deploying large-scale information systems. Our comprehensive curriculum equips you with the skills and knowledge to develop the layers of technology involved in the next generation of massive information system deployments and analyze the data these systems generate. When you graduate, you’ll have a unified vision of these systems from your core courses; internship experience; and semester-long, group-oriented capstone project. MCDS graduates are sought-after software engineers, data scientists and project managers at leading information technology, software services and social media companies.

The MCDS program offers three majors: Systems, Analytics, and Human-Centered Data Science. All three require the same total number of course credits, split among required core courses, electives, data science seminar and capstone courses specifically defined for each major. The degree can also be earned in two different ways, depending on the length of time you spend working on it. Regardless of the timing option, all MCDS students must complete a minimum of 144 units to graduate.

Here are the options:

  • Standard Timing — a 16-month degree consisting of study for fall and spring semesters, a summer internship, and fall semester of study. Each semester comprises a minimum of 48 units. This timing is typical for most students. Students graduate in December.
  • Extended Timing — a 20-month degree consisting of study for fall and spring semesters, a summer internship, and a second year of fall and spring study. Each semester comprises a minimum of 36 units. Students graduate in May.

Core Curriculum

All MCDS students must complete 144 units of graduate study which satisfy the following curriculum:

  • Five (5) MCDS Core Courses (63 units)
  • Three courses (3) from one area of concentration curriculum (36 units)
  • Three (3) MCDS Capstone courses (11-635, 11-634 and 11-632) (36 units)
  • One (1) Electives: any graduate level course 600 and above in the School of Computer Science (12 units)

Area of Concentration

  1. During the first two semesters in the program, all students take a set of five (5) required core courses: 11-637 Fundamentals of Computational Data Science, 15-619 Cloud Computing, 10-601 Machine Learning, 05-839 Interactive Data Science, and 11-631 Data Science Seminar.
  2. By the end of the first semester, all students must select at least one area of concentration — Systems, Analytics, or Human-Centered Data Science — which governs the courses taken after the first semester.
  3. To maximize your chances of success in the program, you should consider which concentration area(s) you are best prepared for, based on your educational background, work experience, and  areas of interest as described in your Statement of Purpose.
  4. You are strongly encouraged to review the detailed curriculum requirements for each concentration area, in order to determine the best fit given your preparation and background.

For a complete overview of the MCDS requirements read the MCDS Course Map.

To earn an MCDS degree, students must pass courses in the core curriculum, the MCDS seminar, a concentration area, and electives. Students must also complete a capstone project in which they work on a research project at CMU or on an industry-sponsored project.

In total, students must complete 144 eligible units of study, including eight 12-unit courses, two 12-unit seminar courses, and one 24-unit capstone course. Students must choose at minimum five core courses. 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.

Students who plan to select the Systems concentration may wish to enroll in 15-513 “Introduction to Computing Systems” during the summer session preceding their enrollment in the program; this course is a prerequisite for many advanced Systems courses, so it should be completed during Summer if you wish to enroll in advanced Systems courses in the Fall.

Click here to see the MCDS Course Map.

Some example courses of study are included below.

Example 1: Analytics Major, 16 Months

 

Fall

Spring

Summer

Year 1

Data Science Seminar

Machine Learning

Machine Learning for Text Mining

Advanced Machine Learning

Design and Engineering of Intelligent Information Systems

Big Data Analytics

Data Science Seminar

Capstone Planning Seminar

Machine Learning with Big Data Sets

Cloud Computing

Information Systems Project

Search Engines

Multimedia Databases and Data Mining

Large Scale Multimedia Analysis

Summer Internship

Year 2

Data Science Analytics Capstone

 

 

 

Example 2: Systems Major, 16 Months

 

Fall

Spring

Summer

 Year 1

Computational Data Science Seminar

Advanced Storage Systems

Cloud Computing

Distributed Systems

Machine Learning

Computational Data Science Seminar

Parallel Computer Architecture and Programming

Advanced Databases

Search Engines

Summer Internship

 Year 2

Computational Data Science Systems Capstone

 

 

 

Operating Systems or Web Applications

   

Example 3: Human-Centered Data Science Major, 16 Months

Example Schedule

Fall

Spring

Empirical Analysis of Interactive Systems

ML
Econometrics
Social Web
Network Science
Business Analytics

Interactive Data Science
Psych Found for Design Impact
Econometrics
DHCS

 Social Web Analytics & Design

ML
ARM
Social Web
Network Science

Crowd Programming
Data Pipeline
ML for Text Analytics
DHCS

Ubiquitous Computing

DHCS
ML
ARM

Interactive Data Science
Rapid Prototyping
Gadgets
Usable Priv & Security
Advanced ML

Educational Software Design

DHCS
ML
ARM
Learning Analytics and EDS

Learning with Peers
Psych Found for Design Impact
ML with Big Data
ML with Text Analysis

Carnegie Mellon's School of Computer Science has a centralized online application process. Applications and all supporting documentation for fall admission to any of the LTI's graduate programs must be received by the application deadline. Incomplete applications will not be considered. Information will be regularly updated HERE.

Fee Waivers

Fee waivers may be available in cases of financial hardship, or for participants in select "pipeline" programs. For more information, please refer to the School of Computer Science Fee Waiver page.

Requirements

The School of Computer Science requires the following for all applications:

  • A GPA of 3.0 or higher.
  • GRE scores: These must be less than five years old. Our Institution Code is 2074; Department Code is 0402. (This requirement is waived for CMU undergrads.)
  • The GRE At Home test is accepted but we prefer you take the GRE at a test center if possible.
  • Unofficial transcripts from each university you have attended, regardless of whether you received your degree there.
  • Current resume.
  • Statement of Purpose.
  • Three letters of recommendation
  • A short (1-3 minutes) video of yourself. Tell us about you and why you are interested in the MCDS program. This is not a required part of the application process, but it is STRONGLY suggested.  
  • Proof of English Language Proficiency

Proof of English Language Proficiency:
If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo. We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored.

We do not issue waivers for non-native speakers of English. In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university. We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States. No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Applicants applying to MCDS are required to submit scores from an English proficiency exam taken within the last two years. For more information about their English proficiency score policies, visit the MCDS admission website. 

Successful applicants will have a minimum TOEFL score of 100, IELTS score of 7.5, or DuoLingo score of 120. Our Institution Code is 4256; the Department Code is 78. Additional details about English proficiency requirements are provided on the FAQ page. 

Applications which do not meet all of these requirements by the application deadline (see above) will not be reviewed.

For more details on these requirements, please see the SCS Master's Admissions page.

In addition to the SCS guidelines, the LTI requires:

  • Any outside funding you are receiving must be accompanied by an official award letter.

No incomplete applications will be eligible for consideration.

For specific application/admissions questions, please contact Jennifer Lucas or Caitlin Korpus.

For more information about the MCDS program, contact Jennifer Lucas or Caitlin Korpus

Jennifer Lucas

Academic Program Manager - MCDS
Office: 6415 Gates & Hillman Centers
Email: jmlucas@cs.cmu.edu
Phone: 412-268-9870

Caitlin Korpus

Senior Academic Program Coordinator- MCDS
Office: 5414 Gates & Hillman Centers
Email: ckorpus@andrew.cmu.edu
Phone: 412-268-7096