Program Overview

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.

Overview

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.

Requirements

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.

• 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.

Curriculum

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.

Example Courses of Study

• 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

Admissions

• 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.
  • 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.
  • 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, 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, IELTS, or Duolingo.
• Successful applicants will have a minimum TOEFL score of 100, IELTS score of 7.5, or DuoLingo score of 120.
• Applications which do not meet all of these requirements by the application deadline will not be reviewed.

Additional Information

For additional information about the MCDS program, including information about course registration for accepted applicants, please view the MCDS Additional Information Page. This page is meant to guide recently admitted students.