A customizable program shaped by your interests.
The Master of Professional Studies (MPS) in the field of Data Science and Applied Statistics provides a solid foundation in theoretical statistics, a broad education in many applied statistics topics, certification in SAS, and a semester-long, real-world data analysis project.
Students can choose between two specialized tracks: Statistical Analysis or Data Science, each with its own focused curriculum and requirements.
Planning your curriculum.
A flexible structure based on your program.
Whether you choose the Statistical Analysis or Data Science track, the program requirements are designed to prepare students for specific career paths while maintaining a strong statistical foundation. The flexibility of these tracks allows students to tailor their education to their career goals while ensuring mastery of fundamental statistical concepts and methodologies.
- Total of 30 credit hours required, all of which must be earned while enrolled in the MPS program (no transfer credits accepted).
- All required courses must be taken for a letter grade.
- Only one elective course per semester may be taken S/U.
- Statistical Analysis track students may take STSCI 5045, 5060, and 5065 as S/U electives; these are required letter-grade courses for the Data Science track.
- Minimum grade requirement: C- or better (or S for S/U courses).
- Minimum GPA requirement: 2.5 for graduation.
- Electives must be from the approved list or approved by the Program Director.
- New electives must be technical courses (5000+ level) with substantial statistics content.
- Duplicate courses covering similar material cannot both count toward degree.
Complete the MPS Applied Statistics and Data Science Application to Graduate form online when you are ready to confirm the courses you have taken for your degree.
While typically completed in one year, students may request a program extension under special circumstances, subject to these conditions:
- Maximum program length is two years.
- Extensions require compelling academic or medical reasons.
- Students must maintain 12 credits during extension semesters.
- Extension requests must include:
- New expected graduation date
- Academic justification
- Planned coursework
- International students must ensure F-1 status remains valid throughout the extension period.
To request course waivers or new electives, complete the Course Waiver - Application for Elective Form and contact aw269 [at] cornell.edu (aw269[at]cornell[dot]edu).
Building upon strong foundations.
Regardless of program track, students are required to take a series of core courses.
- STSCI 5030: Linear Models with Matrices (4 credits) Fall
- STSCI 5080: Probability Models and Inference (4 credits) Fall
- STSCI 5954: Project Development & Professional Communication (2 credits) Fall
- STSCI 5955: Real Time Project Management (1 credit) Spring
- STSCI 5999: Applied Statistics MPS Data Analysis Project (4 credits) Spring
Additional Required Courses for Data Science Track:
- STSCI 5045: Python Programming and its Applications in Statistics (4 credits) Fall
- STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits) Fall
- STSCI 5065: Big Data Management and Analysis (3 credits) Spring
Statistical Science Electives:
- STSCI 5010: Applied Statistical Computation with SAS (4 credits)
- STSCI 5040: R Programming for Data Science (4 credits)
- STSCI 5045: Python Programming and its Applications in Statistics (4 credits)
- STSCI 5050: Modern Regression Models for Data Science (4 credits)
- STSCI 5060: Database Management and SAS High Performance Computing with DBMS (4 credits)
- STSCI 5065: Big Data Management and Analysis (3 credits)
- STSCI 5090: Theory of Statistics (4 credits)
- STSCI 5100: Statistical Sampling (4 credits)
- STSCI 5111: Multivariate Analysis (4 credits)
- STSCI 5140: Applied Design (4 credits)
- STSCI 5160: Categorical Data (3 credits)
- STSCI 5520: Statistical Computing (4 credits)
- STSCI 5270: Introduction to Survival Analysis and Loss Models (3 credits)
- STSCI 5550: Applied Time Series Analysis (4 credits)
- STSCI 5600: Integrated Ethics in Data Science (2 credits)
- STSCI 5610: Data Science in Risk Modeling (2 credits)
- STSCI 5630: Operations Research Tools for Financial Engineering (4 credits)
- STSCI 5640: Statistics for Financial Engineering (4 credits, forbidden overlap with STSCI 5630
- STSCI 5720: Applied Neural Networks (2 credits)
- STSCI 5725: Data Analysis with Tree-Based Methods (2 credits)
- STSCI 5740: Data Mining and Machine Learning (4 credits), forbidden overlap with CS 5780, ORIE 5740 or ORIE 5741
- STSCI 5750: Understanding Machine Learning (4 credits)
- STSCI 5780: Bayesian Data Analysis: Principles and Practice (4 credits)
- STSCI 6520: Statistical Methods I (4 credits)
- STSCI 6780: Bayesian Statistics and Data Analysis (3 credits)
Additional Approved Electives:
Note: These courses count toward the 30 required credits but do not fulfill Statistical Science Elective requirements.
- AEM 7100: Econometrics I (3 credits)
- BIOCB 6381: Biomedical Data Mining and Modeling (3 credits) - Fall only
- BIOCB 6840: Computational Genetics and Genomic (4 credits) - Fall only
- CS 5740: Natural Language Processing (4 credits)
- CS 5780: Machine Learning (4 credits)
- ORIE 5160: Topics in Data Science and OR (3 credits)
- ORIE 5510: Introduction to Engineering Stochastic Processes I (4 credits)
- ORIE 5580: Simulation Modeling & Analysis (4 credits)
- ORIE 5581: Monte Carlo Simulation (2 credits)
- ORIE 5600: Financial Engineering with Stochastic Calculus I (4 credits)
- ORIE 5610: Financial Engineering with Stochastic Calculus II (4 credits)
- ORIE 5741: Learning with Big Messy Data (4 credits)
- ORIE 6500: Applied Stochastic Processes (4 credits)
- ORIE 6741: Bayesian Machine Learning (3 credits)
Preparing students for work in industry.
Each MPS student completes a two-semester project, which is supported by core courses. The project involves large-scale data analysis and is often completed in collaboration with a private company. View samplings of recent projects that received Best Project Awards.
If you wish to see the full archive of previous projects, please email us at aw269 [at] cornell.edu .
Project Title: Predicting Metastatic Colorectal Cancer from Medicare Claims: a Rule-Based Machine Learning Approach
Team members: Liyongxin Huang, Xinyi Wang, Yixiang Yuan, Daocheng Zhang, and Yuke Zhao
Advisor: Martin Wells
Client: Trinity Life Sciences
Overview: Based on Medicare claims data, the team developed a rule-based verification method for identifying potential patients given their entire claim history records.
Team members: Jessica Rizzo, Yihang Hu, Haotian Xiang, Mingtao Jia, and Haoyang Wang
Advisor: David Ruppert
Overview: This team worked with a leading investment management firm to develop statistical and machine learning models for company use.
Project title: Forecasting Wheat Production in Morocco
Team members: Yudong Fang, Mingyi Hou, Simin Le, Yunhao Li, Jingyan Liu, and Hanyue Yao
Advisor: Ahmed El Alaoui
Client: Gro Intelligence
Overview: This team explored the potential to predict annual winter wheat production in Morocco – a major wheat producer in Africa – using historical data on precipitation, land temperature, soil moisture, and other factors. They found that two prediction models, “sliding window” and Long Short-Term Memory (LSTM), appear to be the best options for informing agricultural decisions concerning wheat. This team impressed judges with its scientific excellence, innovative approach, and clear presentation style.
All students in the MPS Program in Applied Statistics need to own a laptop computer running Windows (Windows 7 Professional). The laptop is needed for classroom use as well as for homework assignments and the MPS project. Visit the Software Licensing site for minimum computer requirements.
Computing facilities available to MPS students:
Cornell Center for Social Sciences