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Management of Enterprise Data Analytics

  • Become a qualified manager of Big Data and predictive analytics.
  • Lead your organization to greater success while enhancing your career.

This certificate was designed to help you become a qualified manager in the burgeoning field of predictive analytics. The courses interweave management, statistics and technology to help you learn the tools and techniques used by leading global practitioners. You might be a business owner, manager, analyst, systems architect, programmer, auditor, researcher or statistician. You'll benefit from case studies, demonstrations, projects, guest lectures and highly experienced instructors. You'll understand how data-driven decision-making can help your organization enhance profitability and better meet customers' expectations. You'll know how to create and defend a business case for analytics and be able to manage analytics with professionalism, insight and skill. This certificate is offered in collaboration with the Faculty of Engineering and Applied Science.

What You'll Learn

  • Manage a Big Data/predictive analytics initiative.
  • Address methodology, quality control, change management and other related matters.
  • Know the important differences between data warehousing, business intelligence and Big Data.
  • Develop security, privacy and risk-management standards.
  • Apply up-to-date procedures, standards and techniques to an ongoing project.

Certificate Requirement(s)

You have three years from the start date of your first course to complete the certificate. Prior Learning Assessment (PLA) is not available for this certificate.

Important: Upon completion of your certificate requirements, you must request your certificate by submitting a Certificate Request Form.

Required Course(s)


Notes

We highly recommend that you have an undergraduate degree and a minimum of three years’ work experience. Good facility with analysis and information technology is recommended, as is familiarity with basic probability and statistics. Estimated workload for most courses is three lecture hours per week, plus an additional 6–10 hours per week of readings, exercises, cases and project homework. If your career goal is to become a data scientist, this program is not the right choice.