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  •  36 Total Units

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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.

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 (SME) 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 from which to draw, 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.

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.

TOTAL UNITS -- 36

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

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

ADVANCED PROGRAM -- 15 UNITS

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

REQUIRED COURSE -- 3 UNITS

MSBA 395
Business Analytics Capstone

ELECTIVE COURSES -- 18 UNITS

Students may select any six of the following courses

MSBA 307
AI for Data 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 330
Self-Service Analytics

CONCENTRATIONS

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

  • Management
  • Marketing

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CONCENTRATIONS

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.

LEAD 300
Management and Leadership
LEAD 303
Teamwork in Organizations
MSBA 322
Master Data Management
MSBA 328
Visualization and Communication
MSBA 330
Self-Service Analytics
PSYCH 345
Organizational Behavior and Development

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.

LEARNING OUTCOMES

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.

BUSINESS ANALYTICS DEPARTMENT

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FeaturedAlumni

Jeremy Bice

Jeremy Bice

MSBA 17

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Logical Position

Derek Gee

Derek Gee

MSBA candidate

Product Manager
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Chinmay Vaidya

Chinmay Vaidya

MS IT Management 15

SAP SD / RAR Consultant
Palo Alto Networks

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Oracle VP on GGU's MS in Business Analytics