Master of Science in Business Analytics
The purpose of the Master of Science in Business Analytics (MSBA) degree program is to present 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.
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.
Golden Gate University's Business Analytics programs are overseen by an advisory board comprising business and academic leaders in the field.
- Rich Clayton, VP Business Analytics Product Group, Oracle
- Tracey Edwards, Managing Principal Global Shared Services & CKO (ret), Deloitte
- Michael Evans, National Managing Director, Newport Board Group
- Richard Harris, VP Software Engineering, Asset Performance Management, GE Digital
- Stuart McGuigan, Chief Information Officer, Johnson & Johnson
Admission to the program is selective and limited. To receive full consideration, applicants must submit all required materials by the preferred application deadline. Completed applications will be reviewed by the committee on a weekly basis and qualified applicants will be notified of their admission on an ongoing basis until the incoming class is completed.
Golden Gate University seeks a well-rounded group of students from diverse educational and professional backgrounds. Ideal candidates will have a balance of both quantitative skills and business skills. Students will be selected based on academic transcripts, a statement of purpose, and a resume.
Preferred Application Deadline*
|Feb 15, 2017|
Enrollment Deposit Due†
|Apr 1, 2017|
|Apr 30, 2017|
Preferred Application Deadline*
|Jun 1, 2017|
Enrollment Deposit Due†
|Aug 1, 2017|
|Aug 27, 2017|
*Applications will be accepted after the preferred deadline on a case-by-case basis.
†A $125 non-refundable enrollment deposit guarantees a spot in the program, permits registration, and will be applied to the first term’s tuition.
Ageno School of Business Courses: $2,925 per 3-unit course.
Tuition varies for other courses. See Tuition & Fees for details.
Graduate Writing Proficiency Requirement
Students admitted to this degree program must meet the graduate writing proficiency requirement within the first nine units of the program by successfully passing the Graduate Writing Proficiency Exam or by completing ENGL 201, Graduate Writing, with a grade of B or better.
Math Proficiency Requirement
Students admitted to this program are expected to possess a level of mathematical skill at least equivalent to MATH 20, Intermediate Algebra.
TOTAL UNITS -- 39
FOUNDATION COURSE -- 3 UNITS
CORE COURSES -- 18 UNITS
- MSBA 300
- Foundations of Business Analytics
- MSBA 301
- Performance Management & Metrics
- MSBA 304
- Database Theory & Data Management Tools
- MSBA 305
- Business Intelligence & Decision Support
- MSBA 307
- Analytics, Intelligence, Security, & Privacy
- MSBA 320
- Tools for Business Analytics
ADVANCED COURSES -- 18 UNITS
BUSINESS ANALYTICS DEPARTMENT
- James Faddy, Adjunct Professor
- David Fickbohm, Distinguished Adjunct Professor
- Arhsad Khan, Adjunct Professor
- Judith Lee, Associate Professor & Department Chair, Operations & IT Management
- Ana Lelescu, Adjunct Professor
- Rao Mikkilineni, Adjunct Professor
- Ross Millerick, Adjunct Professor
- John F. Morales, Adjunct Professor
- Piradee Nganrungruang, Adjunct Professor
- Lance Robins, Senior Adjunct Professor
- Mona L. Sabuco, Distinguished 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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