Rensselaer's Education for Working Professionals

Production Analytics Graduate Certificate


275 Windsor Street

Hartford, CT 06120

United States



Program Information

Degrees Offered:

Production Analytics Graduate Certificate

Format: Online

Program Description:

Global supply chains and logistics require data analytics expertise to achieve organizational performance. Today’s cutting-edge organization simply can’t survive without using intelligence generated data to constantly monitor and improve operations. As a leader, you need the power of data analytics to ensure you are making the right decisions. Rensselaer’s Production Analytics Graduate Certificate prepares you to model the organization accurately, differentiate true intelligence from “hunches”, turn unknowns into knowns, and make the best use of data possible.

Certificate Information

  • You can begin the certificate in January, May, or August
  • Each course is 3 credit hours, the certificate is 3 course, 9 credit hours to complete
  • The certificate’s blended online delivery reflects today’s dynamic workplace
  • Practitioner faculty guide the completion of project work that relates to your career objectives
  • The program results in a graduate level Certificate in Production Analytics from Rensselaer Polytechnic Institute.
  • Courses are taken for credit, and successful completion requires a GPA of 3.0 or greater
  • As each of the courses build upon prior courses, courses must be taken in sequence.
  • Students completing the Production Analytics Certificate are not eligible to complete the Business Intelligence (BIH) or Health Analytics (HAH) certificates.
  • Students may choose to complete multiple certificates, or apply certificates to other degree programs, according to each program’s requirements (for eligible programs, see program web site

Course Coverage:

The Production Analytics Certificate requires three courses:

ENGR 6200: Data-Driven Decision Making - Frame questions and resolve problems using data wrangling tools to prepare methods for analysis; employ models using linear/nonlinear multivariate methodologies using R, Python and Excel; validate results and develop algorithms that can be used to make recommendations and forecasts; and, work with stakeholders to scope and frame questions and problems so that actionable results can be achieved.

ENGR 6205: Production and Logistic Analysis - Use visualization and cluster analysis tools to gain deeper insights into production and logistic relationships. Apply data analytic processes to real-world production problems and questions, including evaluating production throughput, factor isolation and output risk analysis, model production changes, forecasting environmental control factor changes, and minimizing defects and shortages. Then, tune and adjust models as underlying assumptions change.

ENGR 6206: Modeling Production Decisions - Working with an instructor as a mentor, develop a big data inquiry model for a production related issue, question or problem of your choice. Over the semester, frame the question to be analyzed, collect and prepare data for analysis, perform the analysis and present actionable results and recommendations back to the organization.



Middle States Commission on Higher Education (MSCHE) The Accreditation Board of Engineering and Technology (ABET) The Association to Advance Collegiate Schools of Business (AACSB) The National Architectural Accrediting Board (NAAB)

Facts & Figures

Classification: Specialized Institution—School of engineering and technology

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