Admissions
- Students applying to Gonzaga University must submit Gonzaga’s Graduate Application, which can be accessed online at https://www.gonzaga.edu/gradapply
- Along with the application for graduate study, each program at Gonzaga has distinct admission requirements. Please refer to the table below to view that detailed information.
|
Program Name |
How To Apply Link |
| Master of Science in Business Analytics | https://www.gonzaga.edu/school-of-business-administration/graduate/msba/how-to-apply |
Foundation Courses
The following foundation (pre-requisite) courses are required for the Master of Science in Business Analytics degree. B+ or higher is required for all pre-requisites.
- MBUS 560 Accounting Analysis (or ACCT 260 and ACCT 261)
- BFIN 320 Principles of Finance
- BMIS 235 Management Information Systems
Additional pre-requisites required for the Accounting Analytics concentration. Accounting prerequisites may be taken after admission into the program and may be waived at the discretion of the program director.
- ACCT 360 Intermediate Accounting I
- ACCT 361 Intermediate Accounting II
- ACCT 363 Cost Accounting
- ACCT 365 Federal Taxation I
- ACCT 464 Auditing
Degree Requirements
A total of thirty (30) credits of graduate coursework is required for the Master of Science in Business Analytics degree. Students must maintain a 3.00 or better grade point average in order to progress through and to graduate from the program. Students have 17 credits of core requirements and must choose one out of two concentrations, Accounting Analytics or Business Analytics.
| MSBA 601 Fundamental Technology for Business Analytics | 3 credits |
| MSBA 622 Data Science for Business | 3 credits |
| MBUS 626 Info Systems Theory and Practice | 2 credits |
| MBUS 613 Quantitative and Stats Analysis | 3 credits |
| MSBA 623 Database Management and SQL | 3 credits |
| MACC 601 Power Query/Power Pivot | 2 credits |
| Choose one of the following concentrations: | |
Accounting Analytics Concentration |
|
| MACC 600 Orientation | 0 credits |
| MACC 611 Audit Analytics | 3 credits |
| MACC 664 Professional Ethics | 2 credits |
| MBUS 633 Team Building Intensive | 2 credits |
| Choose one of the following two courses: | 1 credit |
|
MTAX 619 Data Automation
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|
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MACC 604 Blockchain in Business
|
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| MBUS 670 Foundations of Project Management | 3 credits |
| Choose one of the following four courses: |
3 credits |
|
MTAX 604 C corporation taxation
|
|
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MTAX 605 Partnership taxation
|
|
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MACC 660 Advance Financial Accounting
|
|
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MACC 662 Accounting Information Systems
|
|
Business Analytics Concentration |
|
| MSBA 602 Communication Skills: Persuasion | 2 credits |
| MBUS 614 Business Ethics | 2 credits |
| MSBA 679 Predictive Analytics | 2 credits |
| MSBA 668 Survey Design | 1 credit |
| MSBA 686 Performance Management | 1 credit |
| MSBA 606 Marketing Analytics | 1 credit |
| MSBA 603 Data Visualization | 1 credit |
| MSBA 604 Machine Learning | 1 credit |
| Choose 3 credits from the following five courses: |
|
|
MBUS 612 Managing People and Performance
|
2 credits |
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MBUS 633 Team Building Intensive
|
2 credits |
|
MSBA XYZ Digital Marketing
|
1 credit |
|
MBUS 681 Pythonic Tech for Financial Data
|
1-2 credits |
|
MBUS 689 Financial Modeling & Data Analytics
|
1 credit |
Equivalent:
MACC 600 - Successful completion
MBUS 600 - Successful completion
MTAX 600 - Successful completion
MBUS 600 - Successful completion
MTAX 600 - Successful completion
This course explores the application of current programming languages to platforms currently work to solve business problems and support business decisions.
Equivalent:
MACC 602 - Taken before Summer 2023
This course focuses on how to present results and findings both verbally and in writing to persuade stakeholders to act.
This course will teach graduate student skills on how to use data visualization to communicate findings of analyses of different stakeholder groups.
Data science and machine learning are increasingly applied in business contexts to automate processes and aid in data-driven decision-making. All managers, even those not intending on applying data science themselves, can benefit from learning about data science and machine learning jargon, approaches, and how common algorithms are utilized for business insights. This course introduces managers to the technical side of data science by covering the major parts of the data mining process, including data collection, data preparation, machine learning modeling, model evaluation, and model deployment. Emphasis will be placed on modeling and evaluation stages, with examples provided using Excel and the Python programming language. Some experience with programming (in any language) is recommended, but not required.
This course introduces ideas around marketing analytics and how to use data to best achieve business goals. In the age of big data, marketing and business professionals are expected to make data-driven decisions that drive revenue and influence quickly and effectively. The purpose of this course is to equip students with basic concepts, skills and techniques of measuring and analyzing marketing programs and creating frameworks for re porting success metrics
An exploration of technologies based to manage financial data flows, date analytics, and date requirements.
A study of the data science process and theory , including data extraction, transformation, modeling and application in the business context.
Equivalent:
MACC 622 - Taken before Fall 2023
Database management using sql, data modeling, project life cycle, data normalization, warehousing and manipulation.
This course explores current trends and emerging tools used to solve business problems and develop strategy.
Prerequisite:
MSBA 601 Minimum Grade: C
This course teaches graduate students how to design marketing and employment surveys to maximize reliability and validity. It will teach students to use scales, demographics, and structures to maximize completion rates.
Introduces business students to different business problems for which business analytics can be applied.
This course addresses current managerial issues related to performance measurement, interpretation, and feedback. We will review research in the area and current popular press articles on the use of performance reviews.
MSBA Special Topics