Master of Science - Data Science and Analytics - Engineering Analytics Concentration

The engineering analytics concentration of the master’s degree in data science and analytics is a 34 credit-hour program that refines your abilities for various engineering and managerial roles across numerous industries for product design, manufacturing, wholesale, logistics and beyond!

Taking Your Future Forward

Analytics are your entry point to becoming a real decision-maker in engineering and supply chain roles. But why go with Bradley? Because you deserve opportunities for research projects and internships that will prepare you for the complexities of product and process design, manufacturing execution, inventory management, and any other issues baked into the industry. As a Bradley graduate student, you’ll be ready.

Graduate Admission Requirements

Learn more about graduate admission standards and application requirements on our Requirements page.

Program Admission Requirements

• Completion of at least one semester of statistics
• One semester of programming class or numerical analysis
• Two semester of calculus
• Linear algebra
• Official GRE score sent directly to the Office of Admission by the testing agency. Bradley’s institutional code for score reporting is 1070.

Graduate Program Requirements

Core Courses - 16 hrs..

• IME 511: Probability & Statistics for Analytics - 3 hrs.
• IME 512: Regression and Experimental Design - 3 hrs.
• CS 560: Fundamentals of Data Science - 3 hrs.
• CS 571: Database Management Systems or IME: 514 Operations Research - 3 hrs.
• MIS 570: Introduction to Business Analytics - 3 hrs.
• BUS 511: Communicating Quantitative Information - 1 hr.

Concentration Required Courses - 9 hrs..

• IME 568: Engineering Analytics I - 3 hrs.
• IME 586: Logistics and Supply Chain Systems - 3 hrs.
• IME 691: Research/Practicum - 3 hrs.
  or MIS 590 Business Analytics Consulting Project - 3 hrs.
  or CS 594 Capstone Project for Data Science - 3 hrs.

Possible electives for the Data Science and Analytics Program include courses required by the other concentrations, or additional courses listed below, or courses approved by the department chair. It is the responsibility of the student to ensure they have met the prerequisites for their elective courses.

• CIS 576: Data Management
• CIS 580: Digital Society and Computer Law
• CS 541: Python for Data Science
• CS 561: Artificial Intelligence
• ECE 565: Engineering Applications of Machine Learning
• IME 501: Engineering Cost Analysis
• IME 526: Reliability Engineering
• IME 561: Simulation of Manufacturing and Service Systems
• IME 578: Engineering Analytics II
• IME 583: Production Planning and Control
• MIS 613: Advanced Algorithms for Business
• MTG 624: Marketing Decision Making
• MTH 510: Numerical Methods I
• MTH 511: Numerical Methods II