PREREQUISITES

All applicants must have a bachelor’s degree from a regionally accredited institution. Prerequisite coursework should provide a solid background in mathematics, statistics and computing. This would include: (1) at least one college-level course in calculus, (2) at least one college-level course in probability and statistics, and (3) at least one college-level course in computer programming using a high-level language such as Python, R, C++, etc. Previous coursework in business is not required, but all applicants are expected to have a general knowledge of economics, finance, accounting, statistics, calculus and management principles.

PROGRAM SCHEDULE

The MS-BA offers a three-semester lock-step program which is approximately one calendar year in duration. The MS-BA degree program requires students to complete 11 courses (33 credit hours), a combination of 9 core courses (27 credit hours), 1 elective course (3 credit hours), and 1 hour of professional development each semester (summer, fall, and spring).

Summer

• QMB 5616  Probabilistic Optimization for Analytics
• ISM 5644  Programming for Analytics
• GEB 5932  Professional Development

Fall

• QMB 5755  Quantitative Methods in Business Analytics I
• ISM 5136  Data Analytics and Mining for Business
• ISM 5560  Data Management in Business Analytics
• ISM 5565  Foundational Concepts for Business Analytics
• GEB 5932  Professional Development

Spring

• ISM 5419  Fundamentals of Data Visualization
• ISM 5569 Business Analytics Capstone
• ISM 5566  Forecasting, Revenue Management & Pricing
• GEB 5932  Professional Development
• Elective (1 required)

Elective course options will include applications of analytical tools in specific business disciplines, such as marketing, human resources, operations, finance or real estate. Options may include:

• ISM 5564  Business Analytics for Competitive Advantage
• MAN 5375  HR Analytics
• MAR 5675  Marketing Analytics
• RMI 5257 Data Analytics in Risk Management and Insurance
• GEB 5944 Graduate Internship

*Note: Program requirements are subject to change. Elective availability may vary from year to year.

The following items should be submitted through the Florida State Graduate Application portal:

• Applicant Statement 
• Current resume/C.V., clearly indicating work experience including dates and positions held, noting full-time or part time employment. Management, business and leadership experience should also be clearly detailed.
• Three (3) letters of recommendation from employers or former college professors that speak specifically to the applicant’s ability to successfully complete the MS-BA program (submitted by the recommenders in the online application)
• Florida Residency Declaration if applicable
• Nonrefundable application fee of $30.00 (see University Application or go to fees.fsu.edu)

The following items should be sent to the Graduate Admissions Office, 222 S. Copeland St./314 Westcott Building, Florida State University, Tallahassee, FL 32306-1410 or to graduateadmissions@fsu.edu:

• One (1) official transcript from all colleges and universities attended (FSU transcripts not necessary for FSU alumni, students)
• Official test scores, if applicable:
  • GMAT or GRE scores are optional and should be submitted if they will enhance the application. Any submitted test scores become part of the application. The code to send GMAT scores to Florida State is PN8K567, and the code to send GRE scores is 5219.
  • English Language Proficiency scores are an FSU requirement for international applicants whose native language is not English or who have not completed an undergraduate or graduate degree from a U.S. institution or other institution where English is the required language of instruction; therefore, it cannot be waived). The code to send TOEFL scores to Florida State is 5219.

INTERNATIONAL APPLICANTS

International applicants should visit gradschool.fsu.edu/admissions/international-admissions for information concerning financial responsibilities, degree equivalency, etc.

English Language Proficiency Exam
International applicants whose native language is not English or who have not completed an undergraduate or graduate degree in an English-speaking country are required to take an English Language Proficiency exam and submit official test results in order to be admitted to Florida State University. The College of Business accepts all of the following examinations taken within the past two (2) years:
•    Test of English as a Foreign Language (TOEFL): a minimum score of 600 on the paper-based test and 100 on the internet-based test
•    International English Language Testing System (Academic IELTS): minimum score of 7
•    Pearson Test of English (PTE): minimum score of 66
•    Duolingo: minimum score of 120
•    Cambridge C1 Advanced Level: minimum score of 180
•    Michigan Language Assessment: minimum score of 55

ESTIMATED PROGRAM COSTS FOR THE 2023-2024 ACADEMIC YEAR

The MS-BA program requires 33 credit hours:

• Florida residents: $479.32 (tuition plus fees) per credit hour. Total estimate program cost is $15,817.56.
• Non-Florida Residents: $1,110.72 (tuition plus fees) per credit hour. Total estimate program cost is $36,653.76.

Note: Costs do not include required books and supplies for courses and are subject to change. Fees in table do not include some per-term flat fees for FSUCard and facilities use. For a breakdown of on-campus student fees and their explanations, visit the university’s Tuition Rates page.

Learn more about why our graduate programs are considered A Smart Choice in Value.

Read more about the college’s financial assistance options for graduate students.

You may also visit Florida State's financial aid website for more information on types of financial aid.

ISM 5136 Data Analytics and Mining for Business
This course will provide a managerial overview of the state of art technologies and techniques that are used to discover rich and existing patterns for generating business value, i.e. “business intelligence” for organizations.

ISM 5560 Data Management in Business Analytics
This course will discuss various data-related issues in business analytics and introduce the best practices, underlying principles, and emerging technologies in data management. Specifically, the course will cover 1) foundational data management concepts, 2) best practices in managing big data, and 3) unstructured data management.

ISM 5564 Business Analytics for Competitive Advantage
The course examines the strategic and managerial foundations of business analytics, its use cases and conceptual considerations. Apart from case-led instruction, this course also provides some hands-on experiences with leading-edge software packages, including IBM’s Watson, Tableau, and two textual analytics tools, Semantria and MineMyText.

ISM 5565 Foundational Concepts for Business Analytics
The primary objective of this course is to prepare graduate students in the Business Analytics graduate program with foundational tools and techniques used in subsequent courses.  The primary (but not exclusive) focus will be on achieving and understanding of the role of applied probability methods in business analytics.

ISM 5566 Forecasting, Revenue Management & Pricing
This course explores how big data can be used for understanding and analyzing customer demand and behavior. First, the class surveys the canonical uses of data to analyze consumer demand — time-series forecasting. There will be a focus on Exponential Smoothing and ARIMA models. Then, we explore the idea that sales is not the same as demand.

ISM 5569 Business Analytics Capstone
This course is designed for students in Business Analytics to demonstrate that they have achieved the learning goals established by the program regarding descriptive, predictive, and prescriptive data analysis. Upon completion of ISM 5569, students should be able to analyze real world data with consideration of practical concerns. Specifically, this course is to provide students with an advanced level of analytical skills that will enable them to examine business problems by developing models, analyzing alternatives, and recommending solutions using techniques and tools they have learned in previous Business Analytics courses.

ISM 5644 Programming for Analytics
This is an introductory course intended to introduce students to the basics of computer programming for business analytics. The course will place special emphasis on utilizing Python programming language for data science and analytics related tasks.

ISM 5419 Data Visualization
Course number and description in development.

MAN 5375 HR Analytics
The course focuses on the analysis and application of a company’s HR data to uncover insights that inform HR strategies, process changes, and investments – with the goal of improving organizational performance (i.e., driving business outcomes). Students will learn about theory and research regarding drivers of employee performance, retention, and engagement, as well as the critical HR metrics that are important for business outcomes.

MAR 5675 Marketing Analytics
The practicing marketing scientist must necessarily have a lot of tools in the toolbox. However it is difficult to learn these tools since they have been adopted from a large number of other fields; like psychology, economics and sociology; each with its own notation. This course then will serve as a survey of the Marketing Analytics field, reducing your startup cost to using the techniques needed by the practicing marketing scientist, and to show how marketing analytic techniques feed into the strategic marketing process and business decision-making in general.

QMB 5616 Probabilistic Optimization for Analytics
This course teaches students techniques to address problems in regression, discriminant analysis, principal component analysis, logistic regression, SEM, etc. Students will utilize methods such as calculus and linear algebra.

QMB 5755 Quantitative Methods in Business Analytics I
This course focuses on deterministic methods of perspective analytics 

RMI 5257 Data Analytics in Risk Management and Insurance
In this course we will focus on the use of data and analytical tools in the insurance industry. We will develop a set of tools for analyzing the types of data used by insurers across various functions including loss estimation, loss reserving, underwriting, and claims. Topics will include exploring traditional and new sources of data, legal and ethical considerations, and challenges associated with forecasting and making inferences in the context of risk and uncertainty.