Today’s business environment challenges firms to use data as a driver in decision making. All sectors of business are bursting with information that needs to be structured and analyzed in order to form meaningful insights.

The Business Analytics Certificate is a 12 credit program that includes a mix of courses related to business analytics designed to give students exposure to computer programming, business intelligence, computer models, and data management in order to foster decision making in the modern enterprise. The certificate is open to all CBE students, including CSB and IBE (see special conditions for CSB, IBE-CSC, IBE-CREG, and IBE-ISE) To earn the certificate students will take at least 12 credits and earn a grade of “C-” or higher in each course.

Learning Objectives

Upon completion of the certificate, students will:

  • Understand the field of data science with an understanding of three distinct areas: predictive (forecasting), descriptive (business intelligence and data mining), and prescriptive (optimization and simulation)
  • Apply data analytic tools in different business disciplines to formulate and solve business problems
  • Demonstrate an understanding of fundamental computer programming constructs and concepts
  • Understand how data is collected, prepared, stored, analyzed, modeled, and visualized for analytical business analysis and decision making

Program Overview

Students are required to complete a minimum of 12 credits.

Required Course* (3 credits)

  • CSE 012: Survey of Computer Science

Exceptions

  • Students with credit for CSE 001, contact Emily Ford for an override to take CSE 012
  • Students with credit for CSE 002 should take CSE 160 (Introduction to Data Science) for the required course, which does not double count towards the elective courses
  • BIS majors are encouraged to take BIS 335 and, subsequently, CSE 160 instead of CSE 012 for the required course; CSE 160 will not double count for the electives

Elective Courses (9 credits) – choose any three of the following from at least two different subject areas

  • ACCT 398: Accounting Data and Analytics
  • BIS 324: Business Data Management
  • BIS 335: Application Development for Business
  • BIS 348: Predictive Analytics in Business
  • CSE 160: Introduction to Data Science
  • ECO/MKT 325: Consumer Insights through Data Analysis
  • ECO 247: Sabermetrics
  • ECO 357: Econometrics
  • ECO 395: Time Series Analysis
  • ECO 367: Applied Microeconometrics
  • FIN 334: Derivatives and Management of Risk
  • FIN 335: Financial Management - Modeling
  • FIN 336: Real Estate Finance
  • MKT 326: Marketing Analytics in a Digital Space

Program Directors

Nevena T. Koukova
Associate Professor of Marketing

Catherine M. Ridings
Associate Professor of Management

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