Data Science and Statistics, MS

Location(s): Main Campus

The MS in Data Science and Statistics program provides students with a solid foundation of practical knowledge to work with applied statistics in depth, preparing them for future careers in the public and private sectors as data scientists, consultants and engineers who manage and analyze data. The statistics component gives broad training. The master’s degree prepares students for jobs as statisticians and data analysts in government and industry. Students are given training in many diverse statistical methods used to analyze data, as well as the mathematical, statistical and probabilistic foundation.

A detailed explanation of the requirements for the master’s degrees can be found in the Graduate Handbook of the department.

Program Admission Requirements

For admission to regular standing, a student should have a bachelor’s degree and have completed (with an average of at least 3.0 gpa) a set of mathematics courses approximately equivalent to those required for a bachelor’s degree at Harpur College with a specialization in mathematics. Entering students having substantial graduate-level training may enter the Mathematical Sciences, PhD program, skipping the MS.

Program Requirements

MS in Data Science and Statistics Course Requirements

Students pursuing the MS in Data Science and Statistics must complete a minimum of 40 credits of coursework at the graduate level. This includes the courses listed below. These courses are generally completed in four semesters. Each student’s program is worked out in consultation with an advisor, under the general supervision of the graduate committee.

Core Requirements:
- One of the following options
  - MATH 500 Probability and Statistics for Data Science OR
  - MATH 501 Probability AND MATH 502 Statistical Inference (more rigorous option)
- MATH 530 Computational Linear Algebra
- MATH 531 Stats Modeling with Regression
Practical Training and Capstone Requirements:
- MATH 534 Practical Data Analysis
- MATH 540 Capstone Seminar I
One course from the following:
- MATH 532 Gen. Linear & Mixed Models
- MATH 535 Advanced Statistical Learning
- MATH 543 Computational Statistics
- MATH 545 Data Science Principles with R
- MATH 546 Scientific Computing in Python
- MATH 556 Experimental Design
- MATH 570 Data Mining Multivariable Analysis
Three or Four elective courses
- Students opting to take MATH 501 and MATH 502 must take three elective courses to fulfill this requirement
- Students opting to take MATH 500 must take four elective courses to fulfill this requirement.

Additional Information About the Program

For more information on the Data Science and Statistics, MS program, please refer to the Department of Mathematics and Statistics website. To apply to the Data Science and Statistics program, please visit the University Admissions website.