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The Master of Science in Business Analytics presents students with an understanding of the many possibilities for applying data analytics to business problems. Data analytics, and the implications of this strategic discipline, give practitioners new opportunities for discovering insights that can support the strategic goals and decision making of the organization. The discipline has grown so fast that it is impossible to address all of its elements, so this degree should be viewed as a "toolkit" of statistical and analytic theory, processes, tools, and techniques, which can be integrated into the business depending on the discipline and needed outcomes.

[GGU now offers a convenient and unique weekend MSBA program. See the “Weekend Program” link at left to learn more.]

The MSBA is relevant to multiple audiences, including: the business manager charged with using data analytics to derive value from data and/or leveraging analytics teams to get that value; the subject matter expert in a business discipline charged with using analytics on the job; the budding business analytics data scientist requiring understanding of a myriad of data analytics tools; and the IT professional responsible for supporting the analytics infrastructure and addressing issues of data security, privacy and ethics. Students completing the MSBA will have earned 39 units including three units of graduate statistics.

Now you can earn the MSBA on a flexible schedule—without cutting into your work week. Our new Saturday-only program allows you to earn your degree over four terms, guaranteed. It’s never been easier to gain expertise in the business analytics field, one of the top 20 fastest growing occupations with job openings expected to increase by almost 3 million in 2021. You’ll enroll with a cohort in Saturday classes offered asynchronously (on your own time) and in real-time Zoom web conferencing. You’ll experience collaborative group projects, field research, networking with your peers, and volunteer opportunities through the Digital Analytics Association. Discounts are available for spouses and dependents of military service members and veterans.


Fall 2021

  • BUS 240: Data Analysis for Managers
  • MSBA 300: SF1 Foundations of Business Analytics
  • MSBA 301: Enterprise Performance Management and Metrics

Spring 2022

  • ITM 304: Managing Data Structures
  • MSBA 320 Advanced Statistical Analysis with R and Python
  • MSBA 328 Visualization and Communication through Storytelling

Summer 2022

  • MSBA 305: Business Intelligence
  • MSBA 326: Machine Learning for Predicitve Analytics
  • MSBA 324: Web and Social Network Analytics

Fall 2022

  • MSBA 327: Natural Language Processing
  • MSBA 330: Self-Service Analytics
  • MSBA 395: Capstone


  • Applicants should hold a bachelor's degree from a regionally accredited US institution or the equivalent from a recognized foreign (outside the US) institution, and provide official transcripts.
  • Applicants whose native language is not English must meet the English Language Proficiency Admission Requirements.
  • Applicants are required to submit a statement of purpose and a resume.
  • Applicants with a bachelor’s degree GPA below 3.0 may also submit master’s degree transcript for consideration.

The Master of Science in Business Analytics requires completion of 36 units in the graduate program and a 3-unit graduate statistics course. Foundation program courses may be waived in accordance with university policy regarding course waivers. The foundation program must be met before starting any degree courses, except MSBA 300.


FOUNDATION PROGRAM -- 3 UNITS (Not included in Total Units)

BUS 240
Data Analysis for Managers
(Must earn a “B” or better before starting advanced program courses.)


MSBA 300
Foundations of Business Analytics
MSBA 301
Enterprise Performance Management & Metrics
ITM 304
Managing Data Structures
MSBA 305
Business Intelligence
MSBA 320
Advanced Statistical Analysis with R & Python


MSBA 395
Business Analytics Capstone


Students may select any six of the following courses

MSBA 307
AI for Data Security, Integrity, and Risk Mitigation
MSBA 321
Big Data Ecosystems
MSBA 322
Master Data Management
MSBA 324
Web & Social Network Analytics
MSBA 326
Machine Learning for Predictive Analytics
MSBA 327
Natural Language Processing
MSBA 328
Visualization and Communication
MSBA 329
Prescriptive Analytics and Optimization
MSBA 330
Self-Service Analytics


Instead of completing 18-units of elective courses, students may complete one of the two program concentrations:

  • Management
  • Marketing



Management Concentration - 18 units

The Management concentration is designed for aspiring and current managers who are charged with using data analytics to derive value from data and for leveraging analytics teams to achieve that value. Emphasis in on applying soft skills to meet these needs.

Required Courses - 9 Units
LEAD 300
Management and Leadership
LEAD 303
Teamwork in Organizations
Organizational Behavior and Development
Elective Courses - 9 Units

Select any three MSBA courses.

Marketing Concentration - 18 units

The Marketing concentration is designed for aspiring and current marketing professionals who are charged with using data analytics to derive value from data and for leveraging analytics to achieve that value

MKT 300
Marketing Management
MSBA 324
Web and Social Network Analytics

Plus four 300-level MKT courses, except MKT 398.


Graduates of the Master of Science in Business Analytics will be able to:
  • Explain the differences between structured and unstructured data, aligning each with appropriate business applications.
  • Articulate and align with corporate performance, the complexities of data management,including organizational structures, data policy, data governance, data ownership,and data strategies.
  • Explain and give examples of the three analytic disciplines of descriptive, predictive,and prescriptive (optimization).
  • Identify and explain the steps of the CRISP-DM process model.
  • Anticipate challenges to data security, privacy and ethics, recommending reasonable solutions to issues when they occur.
  • Recognize the challenges of Big Data and describe the use of supporting technologies.
  • Use visual outcomes of analytics to communicate effective messages to members of the business community.
  • Describe the different approaches to machine learning, demonstrating application of the most common algorithms.
  • Explain Natural Language Processing, identifying potential uses and challenges.
  • Interpret and analyze individual business problems, selecting the best analytic approach and appropriate tools for extracting value from the data.
  • Explain the differences between the R and Python programming languages and demonstrate proficiency in each.
  • Promote data quality by effectively acquiring, cleansing, and organizing data for analysis.
  • Plan and implement the use of self-service analytics in the workplace, addressing the challenges of stand-alone implementations.


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Chinmay Vaidya

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Oracle VP on GGU's MS in Business Analytics