Master of Science in

Business Analytics

Big Data for Big Business

Data Analytics is a field of explosive growth and burgeoning opportunity. As organizations scramble to find qualified employees, savvy professionals are looking to cash in on the demand by establishing themselves in the field. Golden Gate University's Master of Science in Business Analytics (MSBA) program opens the door to this lucrative world by providing courses that are designed for application of analytics to address real-world business challenges.

Online
San Francisco
Hybrid

1090 /unit
(See other rates)

36 Total Units

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Yes

Do you plan to study on an F or J Student Visa?

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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.

PROFICIENCY REQUIREMENTS

Math Proficiency

Intermediate Algebra (Math 20); See Graduate Mathematics Proficiency Requirement for more information.

Writing Proficiency

Students are expected to possess proficiency in writing to ensure they can be successful in their course of study. Students may meet this requirement by satisfying one of the screening criteria listed under Graduate Writing Proficiency Requirement.

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

CONCENTRATIONS

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

Management
Marketing

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Projected Schedule

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

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

Andrey Aredakov, Adjunct Professor
James Faddy, Adjunct Professor
David Fickbohm, Distinguished Adjunct Professor
Arhsad Khan, Adjunct Professor
Judith Lee, Associate Professor & Department Chair, Business Innovation & Technology
Ana Lelescu, Adjunct Professor
Paloma Mejia, Adjunct Professor, Business Analytics
Rao Mikkilineni, Distinguished Adjunct Professor
John F. Morales, Adjunct Professor
Piradee Nganrungruang, Adjunct Professor

Lance Robins, Senior Adjunct Professor
Mona L. Sabuco, Distinguished Adjunct Professor
Heinz Joerg Schwarz, Senior Adjunct Professor
Joerg Schwarz, Adjunct Professor
Jane Sherman, Adjunct Professor
Nabanita Talukdar, Visiting Assistant Professor and Director of Math Programs
Bhanu Viswanadha, Adjunct Professor
Helen Yelluas, Adjunct Professor
Sia Zadeh, Professor

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Advisory Board

GGU's Business Analytics programs are overseen by leaders from powerful organizations.

Tracey Edwards
Managing Principal Global Shared Services & CKO (ret)

Rich Clayton
Vice-President Business Analytics Product Group

June Dershewitz
Head of Data Analytics & Governance

Dr. Zoher Karu
Managing Director
Head of Data and Analytics, APAC & EMEA

David McGuire
Sr. Director
Global Media & Marketing Ops

Stuart McGuigan
Chief Information Officer

Richard Harris
VP Digital Product Engineering

Michael Evans
National Managing Director

A Powerful Network

0 Alumni

At Top Organizations

In High-Level Positions

Around the World

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FeaturedAlumni

Jeremy Bice

MSBA 17

Digital Marketing Analyst
Logical Position

Derek Gee

MSBA candidate

Product Manager
Workday

Chinmay Vaidya

MS IT Management 15

SAP SD / RAR Consultant
Palo Alto Networks

The MSBA is taught by business professionals, for business professionals.

Rich Clayton VP Oracle

The machine learning class in the MSBA program has shown me how powerful predictive analytics can be in identifying areas in the business that can be improved upon that business leaders often miss.

Derek Gee MSBA Candidate