Masters in Data Science & Applied Statistics (MPS)

Where statistics meets science and society.

With a Master of Professional Studies (MPS) in Data Science and Applied Statistics, you'll gain advanced technical skills that prepare you for data-driven roles across nearly every industry.

One program. Two options.
	APPLIED STATISTICS	DATA SCIENCE
Overview	This option places emphasis on the techniques of applied statistics.	Along with statistical techniques, this option emphasizes computer science, including scripting, databases, big data and high-performance computing.
Degree Differences	Applied statistics primarily focuses on statistical analysis techniques.	The data science option expands to the knowledge and skills of computer science that are critical to data science.

Strengthening your expertise.

The MPS program strengthened my foundations in probability, statistics, and machine learning, which are essential for the analytical and modeling work I do daily. Additionally, I gained more practical skills in Python and SQL through the program's diverse coursework, both of which are critical tools in my role.

Minghan Liang, MPS '22

Only at Cornell: The Ivy's Only Data Science MPS

As the only Ivy League that offers this program, the MPS in Data Science and Applied Statistics can be completed in two semesters*. Applicants with strong mathematical backgrounds are preferred, as the program is technical and mathematically rigorous. Those with undergraduate degrees in statistics, applied mathematics, engineering, or computer science are well-prepared for this next step.

*While exceptions are sometimes made, attending for a third semester is not guaranteed.

Digging into the data.

Year to complete this two-semester program

72%

Recent graduates who are employed

$101K

Mean starting salary for recent graduates

32%

Alumni employed in the finance sector

VIEW COLLEGE OUTCOMESVIEW COLLEGE OUTCOMES

Careers in Data Science and Applied Statistics

Explore Bowers Career Planning

Turning big data into bold predictions.

Applies advanced statistical methods and machine learning algorithms to extract insights from complex datasets while developing predictive models and conducting hypothesis testing.

Transforming raw data into business intelligence.

Uses statistical techniques to clean, analyze, and visualize data sets while creating reports and dashboards to support data-driven decision making.

Crafting statistical solutions for complex problems.

Develops and applies statistical methodologies to analyze data, design experiments, and draw meaningful conclusions from research studies.

Powering decisions through statistical rigor.

Conducts statistical analyses and hypothesis testing to solve specific business problems while ensuring statistical validity of findings.

Bridging data and business strategy.

Uses statistical methods to analyze business processes, market trends, and performance metrics to drive strategic decision-making.

Calculating risk through statistical mastery.

Applies probability and statistical methods to assess risk and uncertainty in insurance and pension programs.

Advancing knowledge through statistical innovation.

Develops and implements complex statistical models to solve research problems and test theoretical frameworks.

Uncovering insights through methodical analysis.

Conducts statistical research to evaluate hypotheses and provide evidence-based recommendations across various fields.

Mastering uncertainty with statistical tools.

Uses statistical modeling to identify, assess, and mitigate risks in business operations and investments.

Building robust pipelines for statistical success.

Designs and maintains the data infrastructure necessary for statistical analysis and machine learning applications.

Driving financial success through statistical insight.

Applies statistical methods to analyze financial data, forecast trends, and evaluate investment opportunities.

Powering AI through statistical intelligence.

Implements statistical and machine learning algorithms to create automated systems and predictive models.

Explore the curriculum.

To earn the MPS in Data Science and Applied Statistics degree, students must complete at least 30 credit hours in required courses and electives.

To design their tailored academic plan, students are encouraged to work with Academic Planning. If you are a Cornell undergraduate interested in the MPS program, please visit the Admissions page to learn about the Early Credit Option.

Core Courses

Program course requirements:

Linear Models with Matrices
Probability Models and Inference
Project Development & Professional Communication
Realtime Project Management
Applied Statistics MPS Data Analysis Project

Additional requirements for the data science track:

Python Programming and its Applications in Statistics
Database Management and SAS High Performance Computing with DBMS
Big Data Management and Analysis

Learn more on Academic PlanNing

Data Science Project

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.

Learn more on Academic PlanNing

Electives

Electives span a wide range of topics central to modern data science and statistics, including: