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1 Year On Campus Masters Program
Carnegie Mellon UniversityThe Master of Statistical Practice (MSP) at Carnegie Mellon University (CMU) is an intensive, practice-focused graduate program that prepares students for applied careers in data science and statistical consulting. Offered by the Department of Statistics & Data Science within the Dietrich College of Humanities and Social Sciences, the program emphasizes hands-on data analysis, communication, and collaboration—bridging the gap between statistical theory and real-world decision-making.
The MSP program is ideal for students seeking advanced training in applied statistics, data science techniques, and client-centered consulting, without committing to the theoretical depth required for a PhD.
Program Format & Duration
Full-time, in-person professional master's program
Duration: 12 months (August to August)
STEM-designated (eligible for OPT for international students)
No thesis required
GRE optional (recommended for international students)
Core Curriculum Components
The MSP program focuses on the practical application of statistical and machine learning methods, emphasizing data interpretation, programming, and clear communication.
Core Courses
Students complete foundational and advanced coursework in:
Statistical Foundations
Probability & Statistical Inference
Linear Models & Regression
Experimental Design
Categorical Data Analysis
Computing & Data Skills
Statistical Computing in R and Python
Data Wrangling & Visualization
Reproducible Research and Git
Big Data Tools & Parallel Computing
Machine Learning & Predictive Modeling
Supervised & Unsupervised Learning
Tree-Based Methods
Model Selection & Validation
Feature Engineering and Ensemble Methods
Statistical Practice & Ethics
Statistical Consulting and Client Interaction
Communicating Data Insights to Non-Experts
Responsible Data Use and Statistical Ethics
Capstone Consulting Practicum
The hallmark of the program, this year-long course simulates real client interactions
Students work in teams on live consulting projects with organizations from healthcare, education, tech, and non-profits
Includes regular feedback, presentations, and formal reporting
Focuses on translating complex data into meaningful recommendations
The MSP program is fully grounded in real-world statistical applications, emphasizing teamwork, communication, and decision-making in data-rich environments.
Client-Facing Practicum
Students consult with faculty, corporate partners, or local institutions
Deliverables include:
Initial project proposal
Analysis scripts and dashboards
Final report and client presentation
Develops soft skills like client communication, expectation management, and ethical reasoning
Workshops & Seminars
Regular skill-building workshops (e.g., Shiny app development, reproducibility, data storytelling)
Guest lectures from alumni, data scientists, and CMU research affiliates
Career Development
Dedicated support for resume building, job search, and technical interview prep
LinkedIn review and mock interviews provided by CMU’s Career & Professional Development Center
The MSP program equips graduates with immediate readiness for data-intensive roles in diverse sectors. The curriculum balances technical capability with a deep emphasis on communication and ethical practice, making graduates highly attractive in industry.
Career Outcomes
MSP graduates typically pursue roles such as:
Top Employers
Graduate Pathways
While the MSP is terminal, some graduates go on to:
Salary Overview-
Top Employers-
Geographic Placement-
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What This Means for You-
Bachelor’s Degree & GPA: Hold a U.S. bachelor’s degree (typically in CS, engineering, math, or related) with strong academic performance (GPA ≈ 3.0 or higher).Transcripts: Upload unofficial transcripts for all postsecondary education; official transcripts and certified English translations (if applicable) are required upon admission.Prerequisite Coursework: Prior to application, complete or have strong background in calculus-based probability/statistics (two semesters), linear regression analysis, and matrix/linear algebra.GRE Scores: Submit GRE General Test scores unless you're a CMU alumnus—scores must be taken within the last five years.English Proficiency (International): Provide TOEFL, IELTS, or Duolingo scores (recommended TOEFL ≥ 105, speaking ≥ 26; IELTS or Duolingo meet threshold). Statement of Purpose: Submit a 1–2 page essay describing your background, motivations, and goals in professional data science.Resume/CV: Include a current résumé summarizing academic, technical, and professional experience.Letters of Recommendation: Provide three recommendation letters (at least two from faculty or recent supervisors).Application Fee: Pay $50 if applying by Nov 3 (rising to $75 after); fee paid during online submission.Completion Deadline: Application must be fully complete (including required scores, transcripts, LORs) by January 15, 2025, for full consideration; late applications may be reviewed only if space remains. WES Evaluation: Like many CMU programs, official transcripts are required, and credential evaluations (e.g., WES) are not a substitute—confirmation from the department is recommended.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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