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.
SCHEDULE:
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
ADMISSION REQUIREMENTS
- 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.
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
View All Graduate Faculty
