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.

The 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.

Ways to Learn

Online
Campus
Hybrid

Estimated Cost

Admission Events

Advisory Board

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

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 At Top Organizations

 In High-Level Positions

 Around the World

Featured ALUMNI

Jeremy Bice

Jeremy Bice

MS, Business Analytics 17

Digital Marketing Analyst
Logical Position

Derek Gee

Derek Gee

MS Business Analytics candidate

Product Manager
Workday

Chinmay Vaidya

Chinmay Vaidya

MS IT Management 15

SAP SD / RAR Consultant
Palo Alto Networks

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A Few of Our Accolades




TOTAL UNITS — 39

FOUNDATION COURSE — 3 UNITS

MATH 240
Data Analysis for Managers

CORE COURSES — 15 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 320
Advanced Statistical Analysis with R & Python

REQUIRED COURSES — 15 UNITS

MSBA 321
Big Data Ecosystems
MSBA 324
Web & Social Network Analytics
MSBA 326
Predictive Analytics & Machine Learning
MSBA 327
Text Analytics
MSBA 395
Business Analytics Capstone Project

ELECTIVE COURSES — 6 UNITS

MSBA 307
Analytics, Intelligence, Security, & Privacy
MSBA 322
Master Data Management
MSBA 328
Data Visualization & Communications through Storytelling

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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.

BUSINESS ANALYTICS DEPARTMENT

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