MS in Data Science

hand and pen pointer, chartAlthough the title “Data Scientist” may not be used formally, there is clearly an increasing need for personnel that have the ability to apply analytical techniques correctly and can identify new approaches to support critical decisions based on data.

David Sirag
Manager and Discipline Chief for Data & Forecasting, P&W Military Engines

Be Prepared for the Power of Big Data

  • Make your data-collection, organization and storage techniques as efficient as they could be.
  • Successfully integrate data from multiple channels, including unstructured channels, into your analyses.
  • Leverage available data to its fullest when making business decisions.
  • Effectively articulate current data analysis insights to key stakeholders.
  • Be part of this growing team.
  • Information = Competitive Advantage.

This tailored program for UTC will help develop more effective methods for data mining, integration, analysis and solutions development. This program brings together the core disciplines needed for developing contemporary data science skills, such as computer science, mathematical sciences and business analysis.

Analytics has helped to optimize designs, supplier selection, material delivery performance, inventories, deployed asset risk and return, predict engine health and reduce business risk.

Raj Subbu, Ph.D.
Analytics Strategy Leader- PW Global Supply Chain

Download the On-Demand WebinarUTC-OD-200x200-icon_rdax_161x161

Learn more about the MS in Data Science for UTC Employees in this free webinar presentation by Peter Huie. 

MS Data Science Admissions Requirements:

  • Bachelor’s degree from accredited university in computer science, mathematics, business, engineering or quantitative sciences.
  • Coursework in in programming, data structures, algorithms, univariate and multivariate calculus, linear algebra and introductory statistics*.
  • 3.0 GPA or higher.

* A strong applicant who is missing background coursework may be provisionally admitted, with the expectation that he or she will take, and pass one or more undergraduate courses in this area of deficiency. These remedial courses will not count towards meeting the M.S. degree requirements.

*GRE or GMAT required for international students; recommended for US students. Waived for WPI students and alumni.

Program Logistics
Online delivery method

  • Master’s degree is 11 courses.
  • 33 credits Courses offer a blend of theoretical, numerical and experimental work.
  • 1 course per semester (10 weeks) 4 courses per year.
  • Intergrative Data Science

    • DS 501. Introduction to Data Science
  • Mathematical Analytics

  • Data Access and Management

  • Business Intelligence and Case Studies

  • Visual Analytics and Decision Making

  • Optimization Methods

    • OIE 598. Optimization Methods for Business Analytics
  • Industrial Engineering

    • OIE 500. Analyzing and Design Operations to Create Value
  • Advanced Mathematical Programming

    • Select One

      • CS 5007. Introduction to Programming Concepts, Data Structures, and Algorithms (strongly suggested for those not comfortable with programming abilities).
      • MA 542. Regression Analysis
  • Business Cases and Finance
  • Capstone Experience

    •  DS 598. Graduate Qualifying Project

Note: The order of the courses listed above does not indicate the order in which they will be taken. Students will be notified about course schedules upon registering.