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 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.
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.