Department of Statistics Unitmark
Dietrich College of Humanities and Social Sciences


The MSP Curriculum

Curriculum Information

The Master's of Statistical Practice degree is an intensive two-semester professional master's 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, marketing, or scientific research, or continuing in graduate school. There is no thesis requirement for this program.

Program Design

To learn more about the pedagogy and design of the MSP curriculum, check out our article in The American Statistician: On Teaching Statistical Practice: From Novice to Expert.

On Teaching Statistical Practice: From Novice to Expert


2018-2019 MSP Schedule

Fall Semester


  • Description: This course covers the principles and practice of Data Science including data input and cleaning, exploratory data analysis, intermediate R programming, beginning SAS programming, beginning to intermediate python programming, and SQL. For Master's in Statistical Practice students only.
  • Key Topics: The process of learning, data science and statistical thinking, EDA, case studies, basic SAS, intermediate R (regular expressions, text processing, dates, reading non-standard data files, debugging, working with S3/S4 classes, writing your own classes), SQL, basic Python.
  • Course Relevance: To aid in the advancement of those skills that help foster an outstanding statistical scientist. Equally important, the broader objective is to develop an appreciation for the general principles of data science that transcend specific languages and to learn how to learn a new programming language.
  • Course Goals: Upon successful completion of this course, students should be able to aid a client in evaluating data that might support the goals and objectives of the client, use appropriate software to collect/clean/store/retrieve data, use appropriate software to perform pertinent exploratory data analysis and use the results to aid in evaluation of data quality and choice of useful statistical models, write clear/safe/well-documented/reusable scripts for data analysis, and write functions to improve the ability of yourself/current/future team members to clean/explore/analyze/evaluate data and models and to communicate results.

  • Description: This course covers a variety of professional skills including resumes and cover letters, writing reports, oral presentations, teamwork, and project planning. Consulting skills are developed in the form of a whole-class consulting project. For Master's in Statistical Practice students only.
  • Key Topics: Elevator pitches, resumes, cover letters, decoding job descriptions, interviewing, collaboration, IMRaD format of writing, good writing practices in different settings, oral presentations, communication with graphics, principles of consulting, group consulting project work.
  • Course Relevance: To develop professional skills that will be of value in the workplace (i.e., to make students highly valued employees). These skills include written and oral communication skills, the ability to work effectively in groups, the ability to plan complex projects, the ability to work well with clients in the role of consultant, and the grasp of a broad perspective on the areas where statistics is applied.
  • Course Goals: Upon successful completion of this course, students should be able to write and speak about statistical work to both lay and technical audiences, produce graphics that communicate effectively, approach group projects with specific ideas about how to work effectively with others, work with a client to understand their goals, discuss strategies and tactics for achieving goals, and communicate progress and conclusions to the client.

  • Description: This course covers the theory and practice of linear models in matrix form with emphasis on practical skills for working with real data and communicating results to technical and non-technical audiences. For Master's in Statistical Practice students only.
  • Key Topics: Simple linear regression models (inference, diagnostics, and remedial measures), multiple linear regression models (inference, diagnostics, and remedial measures), analysis of variance, analysis of covariance, variable selection, and extensions of traditional linear regression models (generalized linear models, penalized regression with ridge/LASSO, semiparametric regression/smoothing).
  • Course Relevance: To aid in the advancement of those skills that help foster an outstanding statistical scientist. It is critical that students learn how to translate scientific or business problems into the language of statistical models and inference, and how to translate the fruits of a statistical analysis back into the language of your employers or collaborators.
  • Course Goals: Upon successful completion of this course, students should be able to properly analyze real-world datasets using linear regression and related methods in both R and SAS, use exploratory data analysis (EDA) techniques to learn salient features of the data, build appropriate models based on your EDA, diagnose any possible violations of model assumptions and, if necessary, apply remedial measures to overcome violations, perform appropriate analytical/inferential techniques to address objectives of a client/colleague, and clearly communicate the results of an analysis to a layperson.

  • Description: A detailed introduction to elements of computing relating to statistical modeling, targeted to advanced undergraduates, masters students, and doctoral students in Statistics. Topics include important data structures and algorithms; numerical methods; databases; parallelism and concurrency; and coding practices, program design, and testing. Multiple programming languages will be supported (e.g., C, R, Python, etc.). Those with no previous programming experience are welcome but will be required to learn the basics of at least one language via self-study.
  • Key Topics: Effective programming practices, fundamental principles of software design, important algorithms, data structures, and representations, and essential tools and methods.
  • Course Relevance: Computing is an essential -- and increasingly important -- part of statistical practice and methodology. Building a broad and solid foundation in computing will pay significant dividends throughout a student's research career.
  • Course Goals: Upon successful completion of this course, students should be able to develop correct, well-structured, and readable code, design useful tests at all stages of development, effectively use development tools such as editors/IDEs, debuggers, profilers, testing frameworks, and a version control system, build small-to-medium scale software system that is well-designed and that facilitates code reuse and generalization, select algorithms and data structures for several common families of statistical and other problems, and write small programs in a new language.

  • 36-666 Course Description: Financial econometrics is the interdisciplinary area where we use statistical methods and economic theory to address a wide variety of quantitative problems in finance. These include building financial models, testing financial economics theory, simulating financial systems, volatility estimation, risk management, capital asset pricing, derivative pricing, portfolio allocation, proprietary trading, portfolio and derivative hedging, and so on and so forth. Financial econometrics is an active field of integration of finance, economics, probability, statistics, and applied mathematics. Financial activities generate many new problems and products, economics provides useful theoretical foundation and guidance, and quantitative methods such as statistics, probability and applied mathematics are essential tools to solve quantitative problems in finance. Professionals in finance now routinely use sophisticated statistical techniques and modern computation power in portfolio management, proprietary trading, derivative pricing, financial consulting, securities regulation, and risk management.
  • 36-667 Course Description: This course is an introduction to the opportunities and challenges of analyzing data from processes unfolding over space and time. It will cover basic descriptive statistics for spatial and temporal patterns; linear methods for interpolating, extrapolating, and smoothing spatio-temporal data; basic nonlinear modeling; and statistical inference with dependent observations. Class work will combine practical exercises in R, a little mathematics on the underlying theory, and case studies analyzing real problems from various fields (economics, history, meteorology, ecology, etc.). Depending on available time and class interest, additional topics may include: statistics of Markov and hidden-Markov (state-space) models; statistics of point processes; simulation and simulation-based inference; agent-based modeling; dynamical systems theory.


Spring Semester


  • Description: This course is a continuation of 36-601 and covers interactive data visualization with Shiny, advanced R programming techniques, intermediate SAS (macros), web scraping, Hadoop, and Spark. For Master's in Statistical Practice students only.
  • Key Topics: In this course, students will learn and become proficient in Python, web scraping, SAS macros, and Hadoop.
  • Course Relevance: To aid in the advancement of those skills that help foster an outstanding statistical scientist. Equally important, the broader objective is to develop an appreciation for the general principles of data science that transcend specific languages and to learn how to learn a new programming language.
  • Course Goals: Upon successful completion of this course, students should be able to use Python to collect/clean/store/retrieve/analyze data, write R/Python code to automatically extract data from web pages, automate tasks in SAS, use several different interfaces to implement a map/reduce strategy to collect data distributed across multiple computers in a Hadoop distributed file system, and ask the appropriate questions to begin to learn a new computer programming language.

  • Description: This course is a continuation of 36-611 and covers additional writing and presentation skills, as well as interview skills. For Master's in Statistical Practice students only.
  • Key Topics: Oral presentations with slideshows, the job search, interviewing, writing in various settings, and approaching a data analysis when standard software is not available.
  • Course Relevance: To develop professional skills that will be of value in the workplace (i.e., to make students highly valued employees). These skills include written and oral communication skills, the ability to work effectively in groups, the ability to plan complex projects, the ability to work well with clients in the role of consultant, and the grasp of a broad perspective on the areas where statistics is applied.
  • Course Goals: Upon successful completion of this course, students should be able to produce an effective slideshow presentation, demonstrate effective interviewing skills, describe whole areas of statistics orally and in writing, and demonstrate how to approach a statistical problem when software is not available.

  • Description: This course covers fundamentals of experimental design including various ANOVA models, Latin squares and factorial and fractional factorial designs. The time series components covers exponential smoothing models and ARIMA, including seasonal models and transfer function models. Special topics are intermittent. For Master's in Statistical Practice students only.
  • Key Topics: EDA of time series, naive time series methodology, time series decomposition, linear filtering, exponential smoothing, ARIMA models, seasonal ARIMA models (SARIMA), transfer functions, intervention analysis, neural network autoregression, experimental design, ANOVA and contrasts, multiple comparisons, power, common factorial designs, and base rate neglect.
  • Course Relevance: To aid in the advancement of those skills that help foster an outstanding statistical scientist. It is critical that students learn how to translate scientific or business problems into the language of statistical models and inference, and how to translate the fruits of a statistical analysis back into the language of your employers or collaborators.
  • Course Goals: Upon successful completion of this course, students should be able to select, execute, and interpret analyses of time series data, advise on experimental design based upon client goals and restrictions, perform and interpret contrast testing and multiple comparison analyses.

  • Description: Data mining is the science of discovering patterns and learning structure in large data sets. Covered topics include information retrieval, clustering, dimension reduction, regression, classification, and decision trees.

  • Description: Students are taught how to structure a consulting session, elicit and diagnose a problem, manage a project, and report an analysis. The class will participate in meetings with industrial and academic clients. For Master's in Statistical Practice students only.
  • Key Topics: Learning to work with the client and fully understanding the client's needs, understanding the data and cleaning it, optionally gathering new data by web scraping or other means, testing various solutions to the client's problem, fully developing one or more solutions, providing interim reports to the client, providing a final written report, a final oral report with slides, and well-documented software.
  • Course Relevance: "Statistical Practice" is the capstone consulting project course of the Master's of Statistical Practice program. The focus of this course is a consulting project. These projects come from companies in and around Pittsburgh as well as on campus. These are real projects, new each year, with clients who are interesting in the results, and no predetermined "solutions." Students work in groups of two or three and are supervised by a faculty member.
  • Course Goals: Upon successful completion of this course, students should be able to demonstrate effective interpersonal skills related working with clients, demonstrate effective skills for data cleaning, analysis, and model verification, demonstrate effective written and oral presentation skills, work effectively in a group setting, demonstrate production of software that is well-documented enough to be useful to a client, grasp the entire arc of the consulting project, and explain how to approach a new project.



Normally all the MSP students take these courses together as a cohort.

One of the hallmarks of the MSP program is our course, 36-726 "Statistical Practice". The focus of this course is a consulting project. These projects come from companies in and around Pittsburgh as well as on campus. A select set of these projects are listed below. These are real projects, new each year, with clients who are interesting in the results, and no predetermined "solutions". Students work in groups of two or three and are supervised by a faculty member.

  • US Department of Labor - "Child Labor in Uganda & Ethiopia"
  • Fox Chapel Area School District - "Benchmark Data Correlation Study"
  • Allegheny General Hospital - "Patient-Centered Analysis of Interventions & Outcomes in Recurrent Respiratory Papillomatosis"
  • CMU Department of Physics - "Galaxy Lookalikes & The Expanding Universe"
  • Pittsburgh Public Schools - "Assessing the Effectiveness of Pre-K Programs"
  • Pittsburgh Police Bureau - "Epidemiology of Homicides in Pittsburgh"
  • Civic Light Opera - "Predicting Ticket Sales"
  • Corporate Sales - "Predicting Sales by Site for a Large Building Supply Company"
  • Analytics - "Automating Question Selection for Online-Questionairres"
  • Healthcare - "Early Detection of Opioid Abuse"
  • Sports Analytics - "Making Best Use of the Hockey Draft"
  • Internet Market Research - "Predicting Consumer Behavior from Online Polling Data"
  • Heinz School - Public Policy - "Is the Death Penalty a Deterrent for Committing Homicide?"
  • Carnegie Mellon: On-line Learning - "Assessing the Effect of On-line Learning"
  • Carnegie Mellon: Psychology - "Relationship Predictors of Care of Diabetes in Couples"
  • Carnegie Mellon: English - "Analysis of Hillary Clinton's Rhetorical Style"


What's next?