Enter OTP
The Master of Computational Data Science (MCDS) at Carnegie Mellon University’s School of Computer Scienceis a premier, STEM-designated graduate program that prepares students to design, build, and deploy large-scale data-intensive systems. Offered through the Language Technologies Institute (LTI), the program focuses on the engineering and systems side of data science, blending software development, data architecture, and advanced analytics.
With deep roots in AI, machine learning, natural language processing, and cloud infrastructure, the MCDS program is tailored for technically strong students looking to drive innovation in high-performance data science environments.
Program Format & Duration
Full-time, in-person program
Duration: 16 months (3 semesters)
Starts: Fall only
STEM-designated (eligible for 3-year OPT for international students)
GRE required (quantitative excellence expected)
Curriculum Structure
Students in the MCDS program choose from three tracks, each with a specialized focus:
1. Systems Track
Focus: Building large-scale distributed systems and data infrastructure
Key Courses:
Cloud Computing
Distributed Systems
Parallel Programming
Big Data Technologies
2. Analytics Track
Focus: Statistical and algorithmic data analysis and modeling
Key Courses:
Machine Learning
Statistical Methods for Data Science
Probabilistic Graphical Models
Deep Learning
3. Human-Centered Data Science Track
Focus: Data visualization, user interaction, and decision support systems
Key Courses:
Information Visualization
Human-Computer Interaction
User-Centered Design
Communication for Data Professionals
Core Requirements (all tracks)
Data Science Seminar
Foundations of Computer Science
Algorithms and Data Structures
Database Systems or Big Data Tools
Capstone Project (team-based)
Electives
Students select electives from across CMU’s rich graduate offerings in:
NLP
Reinforcement Learning
AI Ethics
Software Engineering
Computational Biology
Robotics and Sensing
The MCDS program emphasizes hands-on, systems-focused training, preparing students to tackle real-world data problems from a computational lens.
Capstone Project
Mandatory, client-driven Capstone Project in the final semester
Teams solve real challenges in collaboration with industry or CMU research labs
Projects span areas such as cloud-scale AI, real-time analytics, privacy-aware computing, and more
Culminates in formal presentation and demo day
Internships
Students typically pursue industry internships during the summer between semesters
Supported by CMU’s robust industry network, including:
Google, Amazon, Meta
Microsoft, Bloomberg, Citadel
Adobe, Palantir, Tesla
AI startups and research labs
Graduates of the MCDS program are known for their technical rigor, systems knowledge, and engineering versatility. They are equipped to develop scalable platforms, optimize machine learning pipelines, and lead innovation at the infrastructure layer of data science.
Career Outcomes
Common roles include:
Machine Learning Engineer
Software Engineer (Data Platforms)
Data Scientist (Research or Product)
AI/ML Infrastructure Engineer
Data Architect
Research Engineer in NLP or Vision
Product Manager (AI Systems)
Top Employers
Google, Microsoft, Amazon, Meta
Nvidia, Tesla, OpenAI
Bloomberg, Capital One, Citadel
Healthcare AI firms, robotics companies, and government labs
Graduate Pathways
Many MCDS graduates pursue:
PhDs in Computer Science, Machine Learning, or NLP
AI or engineering leadership programs
Research roles in labs or think tanks
Startup and entrepreneurial ventures in ML infrastructure or data products
Placement & Salary Overview
Placement Rate: Nearly 100% of graduates secure positions upon graduation
Starting Salary Range: $110,000–$130,000
High-End Offers: Some graduates receive offers exceeding $150,000
Early-Career Salary (All CMU Majors): ~$102,000 average
Career Entry Point: Strong technical roles in AI, ML, and large-scale data engineering
Top Employers
Carnegie Mellon MCDS alumni work at leading global employers across tech and finance, including:
Tech Giants: Amazon, Apple, Google, Microsoft, Yahoo, LinkedIn, Oracle, Salesforce, VMware
Finance & Enterprise: Bank of America, CBS Interactive, Medallia, NetApp
Public Sector: Government and research institutions in the U.S. and abroad
Geographic & Industry Placement
Geographic Focus:
Graduates are placed in major U.S. tech hubs and financial centers, including the Bay Area, New York, Seattle, and Boston
Key Industries:
Tech / Cloud Infrastructure
Finance & FinTech
Enterprise Software
AI/ML Product Teams
Government & Research Labs
Career Progression & Program Strengths
The MCDS curriculum emphasizes software engineering, large-scale data systems, and AI deployment
Graduates typically begin as:
Data Scientists, Machine Learning Engineers, Data Engineers, or Analytics Engineers
Career progression often leads to:
Senior Engineering Roles, AI/ML Specialists, or Technical Leadership Positions
Summary Table
| Aspect | Details |
|---|---|
| Placement Rate | Nearly 100% |
| Starting Salary Range | $110K–$130K (some >$150K) |
| CMU Early-Career Avg | $102K (all majors) |
| Common Roles | DS, ML Engineer, Data/Analytics Engineer, Research Scientist |
| Top Employers | Amazon, Google, Apple, Microsoft, Bank of America, Salesforce |
| Industries | Tech, Finance, Enterprise Software, Government |
| Career Path | Technical roles → Senior/Leadership positions |
What This Means for You
Exceptional job placement with competitive salaries, often exceeding $110K
Strong foundation in data systems engineering, ideal for roles in AI, cloud, and infrastructure
Top-tier employer access and diverse industry options—from Big Tech to public service
Fast-track career progression from technical roles to strategic and leadership positions in AI and data science
Bachelor’s Degree & GPA: Hold a bachelor’s degree (typically in CS, CS‑eng, or related field) from a recognized institution, with strong academic performance (GPA ≥ 3.0 typical) GRE Scores: Submit recent GRE General Test scores (less than five years old), unless you earned/are earning a CMU degree (which waives this requirement)Transcripts: Upload unofficial transcripts from all post‑secondary institutions (official versions required upon enrollment) Statement of Purpose: Provide a concise 1–2 page essay describing your interests, experience, and objectives related to the programResume/CV: Submit a current résumé highlighting education, professional or research experience, publications, honors, and technical skillsLetters of Recommendation: Provide three letters, at least two from faculty or recent employers familiar with your academic/work performance English Proficiency (International Applicants): Submit TOEFL iBT≥100, IELTS≥7.5, or Duolingo ≥120 if English is not your native language, with no waivers allowed based on previous English-medium study Optional Intro Video: Upload a short 1–3 minute video introducing yourself and your interest in MCDS—strongly recommended but not required Application Fee: Pay the fee during application (early‑deadline fee US $80; standard fee US $100)Deadlines: All application materials must be submitted by the posted Fall-cycle deadline (typically in November or early December).Three‑Year Bachelor’s Degrees: CMU requires a U.S.-equivalent bachelor’s degree, and three-year degrees should be evaluated for equivalency—applicants may need to confirm this with admissions if using a shorter program .WES Evaluation: WES or similar credential evaluations are not accepted in lieu of official transcripts, which must be submitted directly from your institution. Data AnalystData ScientistBusiness AnalystStatisticianStatistical ProgrammerSAS ProgrammerMachine Learning EngineerDeep Learning EngineerAI ResearcherData EngineerBI DeveloperData ArchitectAnalytics ConsultantAdvisory ConsultantQuantitative AnalystRisk AnalystOperations AnalystMarketing AnalystResearch ScientistInsights AnalystForecasting AnalystHealthcare Data AnalystFinancial Data AnalystProduct Analyst
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Carnegie Mellon University
