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The Master's in Statistical Practice Program

Program Description

The Masters degree in Statistical Practice (MSP) is a one-year, two-semester professional masters degree program that emphasizes statistical practice, methods, data analysis and practical workplace skills. The MSP is for students who are interested in professional careers in business, industry, government, or scientific research. Students who complete this program will be well trained in the practice of statistics and will be very competitive in the job market. Successful completion of the degree will be grade based. There will not be a masters thesis requirement nor a qualifying exam.

Curriculum

The curriculum will consist of approximately 8 courses. The emphasis of the program will be primarily on statistical methods, data analysis, and professional development.

Data Analysis and Methods Core (~50%): Emphasis will be on applied linear and non-linear models; supervised data analysis; model diagnostics and sensitivity analyses; communicating analysis results. Methods course will include topics in continuous and discrete multivariate analysis, survey sampling, time series, and biostatistics.

Professional Development Core (~25%): Topics to include: Communication skills - both written, oral, and web design; Computing skills - SAS and data base management; Professional and research ethics; Resume writing and interview skills; A data analysis portfolio; Introduction to consulting; Supervised consulting experiences; Careers in Statistics Speaker Series.

Theory Core (~25%): Emphasis will be on the theory of probability and mathematical statistics that form the foundations for statistical methods and practice.

Application

The MSP application deadline is February 15, 2012.
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Prerequisites

  • Two semesters of calculus based probability and mathematical statistics

    Topics should include: random variables, distribution functions, joint and conditional distributions, functions of random variables & their probability distributions; maximum likelihood estimation, properties of estimators, hypothesis testing, interval estimation.
    Equivalent CMU classes are: 36-225 and 36-226
    Typical Textbook: Mathematical Statistics with Applications - Wackerly, et al.

  • One course in advanced data analysis and regression methods

    Topics should include: exploratory data analysis, linear regression models, validation and interpretation of models
    Equivalent CMU classes are: 36-401
    Typical Textbook: Applied Linear Regression Models - Kutner, et al.

  • One course in matrix algebra

Tuition

Graduate Tuition for AY 2011-2012 is: $35,850 (2012-2013 TBA)

Financial aid may be available on a limited basis and will be based on need.
Work opportunities in the Statistics Department will be available on a limited basis.

Additional information about financial aid can be found at
http://www.cmu.edu/finaid/graduate/index.html
and the Graduate Student Financial Assistance Guide

Contact

For more information contact Professor Joel Greenhouse (msp-cmu @ stat.cmu.edu).