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 30 credit-hour program that refines your abilities for various engineering and managerial roles across numerous industries for product design, manufacturing, wholesale, supply chain 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 calculus
  • Official GRE score sent directly to the Office of Admission by the testing agency. Bradley’s institutional code for score reporting is 1070.  Applicants may request a GRE waiver under certain circumstances.  Consult with university admissions.

Graduate Program Requirements

Core Courses - 15 hrs.

  • IME 511: Probability & Statistics for Analytics - 3 hrs
  • CS 541: Python Programming for Data Science – 3 hrs or CS 560: Fundamentals of Data Science - 3 hrs
  • CS 571: Database Management Systems or IME 568: Engineering Analytics I - 3 hrs.
  • MIS 573: Data Visualization for Business Analytics - 3 hrs.
  • MIS 590 Business Analytics Consulting Project – 3 ch OR (CS 594 Capstone Project for Data Science - 3 ch OR CS 699 Thesis) OR (IME 690 Capstone Project for Engineering Analytics OR IME 691 Research/Practicum – 3 ch OR IME 699 Thesis)

Concentration Required Courses - 15 hrs.

Take three out of the following four courses, that you have not taken to fulfill the common core.

  • IME 514 Introduction to Operations Research - 3 ch
  • IME 561 Simulation of Manufacturing & Service Systems – 3 ch
  • IME 586 Logistics and Supply Chain Systems - 3 ch
  • ECE 565 Engineering Applications of Machine Learning – 3 ch

Electives (2 courses or 1 course for thesis students):
Two electives (6 ch) or, for those who choose the thesis option instead of capstone, one elective (3 ch). Electives must be approved by the student’s graduate advisor.

Possible electives for the Engineering Analytics concentration 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 560 Fundamentals of Data Science
  • CS 561 Artificial Intelligence
  • CS 562 Machine Learning
  • CS 563 Knowledge Discovery and Data Mining
  • CS 571 Database Management Systems
  • CS 572 Distributed Databases and Big Data
  • ECE 565 Engineering Applications of Machine Learning
  • ECO 519 Econometrics
  • IME 501 Engineering Cost Analysis
  • IME 514 Introduction to Operations Research
  • IME 526 Reliability Engineering
  • IME 561 Simulation of Manufacturing & Service Systems
  • IME 568 Engineering Analytics I
  • IME 578 Engineering Analytics II
  • IME 583 Production Planning and Control
  • MIS 613 Advanced Algorithms for Business
  • IB 502 Global Trade Management and Analysis
  • MTG 502 Logistics Tools and Techniques
  • MTG 506 Marketing Analytics
  • MTG 507 Customer Analytics
  • MTG 624 Marketing Decision Making
  • MTG 640 Obtaining, Analyzing, and Applying Marketing Information
  • MTH 510 Numerical Methods I
  • MTH 511 Numerical Methods II
  • Q M 526 Business Forecasting
  • Q M 564 Decision Support Systems