Earn an Online Master of Science in Business Analytics in 18 months
Learn to read data from a people-centered school. Numbers are important but so are the people behind them. By learning Business Analytics from The Bill Munday School of Business at St. Edward’s University, you are preparing yourself to think critically, to analyze problems and to make responsible and strategic decisions, all through a lens of moral reasoning and ethics that is unique to St. Edward’s University.
The faculty at St. Edward’s work to strike the ideal balance between theoretical and practical skills. They will teach you to collect data, analyze it and then forecast the future. You will learn to be descriptive, diagnostic, predictive and prescriptive through real-world case studies from across a number of sectors and functional areas of business. Our faculty care about your success and they do their best to equip you with the tools you need to navigate complex scenarios where data analysis can be utilized to the benefit of customers, employees, and the enterprise.
Analyze your career possibilities with a master's in business analytics from St. Edward's. Earning an MSBA can create and enhance an array of career opportunities. Graduates of the program are well-positioned for roles including:
- Management Analyst
- Operations Research Analyst
- Marketing Analyst
- Data Scientist
- Principal or Senior Data Analyst
For further information, please contact an Enrollment Counselor at (512) 326-7501
Message from the Program Director
Thank you for your interest in the Master of Science in Business Analytics (MSBA) program at St. Edward’s University. A Master degree in Business Analytics will prepare you for a rewarding career path as business analytics is one of today’s fastest-growing professions. Organizations, big and small, in virtually all segments of the economy increasingly rely on data analysis to boost process efficiency, enhance customer (and employee) satisfaction, increase product/service innovation, mitigate risk, and achieve all-around performance excellence.
The MSBA program is designed for individuals from a limitless range of educational and work backgrounds, but who have in common a strong interest in problem solving, critical thinking, methodical analysis, intellectual curiosity, and effective communication. It is not assumed that you enter the program with any particular computer programming skills or mathematical knowledge beyond basic algebra.
The four stages of business analytics in which you will be versed include: descriptive analytics - summarizing and characterizing past outcomes; diagnostic analytics – determining the factors and events that contributed to past outcomes; predictive analytics – forecasting likely outcomes with and without changes being made; and prescriptive analytics – solving for the changes that will maximize good outcomes and minimize bad ones.
Your instructors have considerable experience in both applying and teaching the concepts, methods, and algorithms to which you will be exposed. With their patient guidance, and your determination, you will gain the skill set needed to successfully carry out, as well as oversee, business analytics projects. I wish you much success in your pursuit of the MSBA degree and a satisfying career in the discipline.
- Dr. John Loucks
Check Out This Helpful Q&A with Program Director John Loucks
The Master of Science in Business Analytics (MSBA) program at St. Edward’s University prepares students to use analytical tools to make data-driven business decisions. Program director John Loucks explains how St. Edward’s gives program graduates key skills for a growing field.
Learning Goals & Outcomes
Learning Goals:
Our MSBA program prepares students for what companies are looking for and will teach students to analyze historical data to look for trends and patterns, diagnose problems, forecast and predict future outcomes and help model scenarios to make data-driven decisions. As graduates of this program, students will:
- Be capable of translating a problem or opportunity described by a layperson to a technical model formulation.
- Have the ability to identify the appropriate analytical tool(s) for a particular objective.
- Know how to execute the most often-used methods for descriptive, predictive, and prescriptive business analysis.
- Be capable of applying analytics to a variety of business functions at different levels for decision making.
- Understand how business analytics contribute to the decision-making process by producing clear courses of action for consideration.
- Be fluent in Tableau, Python, R, Power BI, SQL, Arena
Outcomes:
The MSBA program is designed with the following overall learning outcomes in mind.
Graduates will be capable of:
- Translating a problem or opportunity described by a layperson to a technical model formulation.
- Executing the most-used methods for descriptive, diagnostic, predictive, and prescriptive analysis.
- Selecting the appropriate analytical method(s) for a particular objective.
- Applying analytics to business decisions in different functional areas and at different planning levels of an organization.
- Contributing to the decision-making process by producing clear courses of action for consideration by managers.
An individual graduating from the program without relevant work experience is expected to immediately qualify for an entry-level position such as junior business analyst. With prior or gained experience, an individual can assume a mid-level role of lead or senior business analyst overseeing teams and large-scale projects. The breadth of functional business decisions covered in the program conceivably makes the MSBA-degreed individual a strong candidate for advancement to high-level administrative positions with titles such as business analytics director and even chief operations officer.
Degree Plan
The online MSBA program is a 30-credit hour program that can be completed in 18 months while working full-time. Courses are offered in a 7-week accelerated online format and taken one at a time. The program culminates with a Final Project Capstone.
Core Courses:
BANA 6310 Introduction to Business Analytics and R
BANA 6312 Data Summarization and Visualization
BANA 6320 Python for Business Analytics
BANA 6330 Big Data and Database Management
BANA 6332 Inferential Statistics
BANA 6340 Predictive Analytics
BANA 6350 Simulation Modeling
BANA 6352 Artificial Intelligence
BANA 6360 Optimization Modeling
BANA 6370 Business Analytics Practicum
Detailed Course Description:
BANA 6310 Introduction to Business Analytics and R
This course presents an introduction into the concepts of data analysis and the tools that are used to perform daily functions. It is broken into two major component parts. The first is to introduce the conceptual framework of business analytics, including the ethical issues and social impact of data analytics. The second is to build familiarity with the basic R toolkit for statistical analysis and graphics, such as data manipulation, exploratory data visualization, and application of fundamental data mining techniques. Prerequisite: None.
BANA 6312 Data Summarization and Visualization
This course introduces students to principles of data visualization and techniques for interactively depicting large datasets. Students learn storytelling and practice with advanced tools to communicate information and data insights clearly and effectively through dashboards. Students gain hands-on experience using interactive data visualization software to create the desired output. Topics include: time series, statistical data graphics, multivariate displays, geospatial displays, dashboards, and interactive and animated displays. Prerequisite: None.
BANA 6320 Python for Business Analytics
Python is one of the highly demanded programming languages for business analysts due to its simplicity, versatility, efficiency, and community support. This course is designed as an introduction to python programming to analyze data. Students will explore fundamental programming with hands-on activities that help them build applications using Python. Topics covered in this course are data wrangling and management, summarizing the data, visualization, statistical analysis, and prediction using data analysis libraries such as Pandas, MatPlotLib, Numpy, Scipy, and more. Prerequisite: BANA 6310, 6312
BANA 6330 Big Data and Database Management
The primary goals of this course are to teach proven techniques for managing organizational data resources and dealing with the 3 V concepts (volume, velocity, and variety) associated with big data. Students learn to design and implement a database using relational database management systems (RDBMS). Students gain step-by- step instruction and hands-on experience with MySQL. Topics include building, modeling and administering a database, data warehousing, data integration, data security, as well as ethical and legal issues surrounding the use of data in our modern society. Prerequisite: BANA 6320.
BANA 6332 Inferential Statistics
This course focuses on the understanding and application of inferential statistics in the decision-making process. Students develop the statistical foundation that underpin business analysis and machine learning. Foundation concepts include probability distribution, interval estimation, hypothesis testing, Bayesian statistics, multivariate analysis (cluster analysis), and statistical control chart. The statistical approach to decision making is based on cutting-edge computer programs and analysis of large-scale data. Hence, the emphasis of this course is placed on combining programming techniques and statistical concepts simultaneously through the analysis of real-life data sets taken from various sources. Prerequisite: BANA 6320.
BANA 6340 Predictive Analytics
This course introduces students to predictive modeling methods and tools. The topics covered include logistic regression, classification and regression tree (CART), random forest, support vector machine, nonparametric kernel estimation, lasso, ARIMA, and text analytics. Students learn how to develop relevant analytic questions and learn multiple methods to evaluate the performance of predictive models to select the most appropriate one. Statistical software packages, such as R or Python, are used for statistical computing. This course helps students internalize a core set of practical and effective skills for projection analysis and apply them to solve real-world problems. Prerequisite: BANA 6330, 6332.
BANA 6350 Simulation Modeling
In this course students learn how to better understand the behavior of a real process that is subject to uncertainty using a model of the process. Simulation models are developed containing the mathematical expressions and logical relationships that are then used to compute the process output values for a given set of input values. Methodically changing process assumptions and operating policies in the simulation model and rerunning it can provide insight to how changes will affect the operation of the real process. Students gain experience in flowcharting a process, developing a computerized simulation model, deciding the experimental design of their analysis, interpreting the output of the computer model, and making recommendations for decision makers. Prerequisite: BANA 6332.
BANA 6352 Artificial Intelligence
This course introduces students to fundamental artificial intelligence (AI) and machine learning concepts and their business applications. The course covers terms, concepts and essential algorithms, including augmented intelligence, knowledge representation and reasoning, machine learning, deep learning, pattern recognition, neural networks, and natural language processing. Students get hands-on experiences with natural language processing technologies. Prerequisite: BANA 6340.
BANA 6360 Optimization Modeling
In this course students learn how to use prescriptive analytics methods classified as optimization approaches. The forms of mathematical programming methods covered including linear and nonlinear, integer and noninteger, deterministic and stochastic, as well as single- and multiple-objective. In the application of the methods to problems in a variety of business functions, students are exposed to all the steps of decision modeling from problem formulation to identification of alternative solutions and the sensitivity of the solutions to the assumptions made. Prerequisite: BANA 6332.
BANA 6370 Business Analytics Practicum
In this course students apply the descriptive, predictive, and prescriptive analytical methods they have studied throughout the program to decision making in different functional areas of an organization including finance, marketing, operations and human resources. Data relevant to a decision are identified, organized, and summarized. Relationships between variables are recognized, and projection models are developed. Best courses of action are determined using tools such as optimization, simulation and artificial intelligence. Students gain practice at not only applying analytical tools, but also communicating the output of the tools in a clear, concise manner. Prerequisites: All other BANA courses.
Austin Connections
The Bill Munday School of Business delivers programs that intentionally leverage the vibrant Austin business environment. Programs and content are grounded in five pillars that capture the essence of the dynamic Austin business ecosystem—entrepreneurial thinking, innovation management, business analytics, social enterprise, and global collaboration. This framework provides a foundation for tackling the world's largest problems.
Austin, Texas is also a great place for business professionals to network, connect and drive up their individual growth. It is a world-class city ranked the #1 fastest growing city with the most job opportunities—this creates great opportunities for our business students!
Technologies
Python (general-purpose programming language)
Pycharm (Python integrated development environment - IDE)
R (statistical computing and graphics package)
RStudio (R integrated development environment - IDE)
Power BI (interactive data visualization package)
SQL (Structured Query Language for databases)
GAMS (General Algebraic Modeling System for optimization)
Arena (discrete-event simulation package)
Excel (spreadsheet package)
Anaconda (open-source DS, AI, and ML software distributor)
Jupyter (interactive computing platform)
Kaggle (online community of data analysts)
Github (Internet hosting service for software development)
Meet the Faculty
Dr. Fatemeh Firouzi
Assistant Professor
Dr. Fatemeh Firouzi is an assistant professor of Business Analytics at St. Edward's University. She earned her Ph.D. in Logistics and Supply Chain Management from Bergamo University in Italy in collaboration with MIT-Zaragoza Logistics Center. Prior to joining St. Edward's University, she served as an assistant professor of professional practice in Business Analytics at Texas Christian University. She has several years of experiences at different universities in Canada and the USA teaching a variety of courses including Data Visualization, Statistical Models, Supply Chain Analytics, Business Statistics, and Operations Management. In addition, she has industry experience in big data analysis and as an industrial engineer.
Dr. Omid Jadidi
Assistant Professor
Dr. Omid Jadidi is Assistant Professor of Operations Management in the Bill Munday School of Business. He has taught operations management, manufacturing management, project management, supply chain management, global management, and other courses with a quantitative or technical emphasis. He holds a PhD in Logistics and Chain Management, and MS and BS degrees in industrial Engineering. His research has been published in leading journals related to the courses he teaches. He also has about five years of industry experience working as a project controller, industrial engineer, and material planning manager.
Dr. Akhil Jonnalagadda
Adjunct Professor
Dr. John Loucks
Professor
Dr. John Loucks holds a PhD (Operations Management major, Business Logistics minor) and an MBA from Indiana University. He earned a BBA (Management Science major) from University of New Mexico. In addition, he has attained ASQ’s Certified Quality Engineer and APICS’s Certified Production and Inventory Manager credentials. Dr. Loucks’ teaching experience includes, in addition to St. Edward’s, positions at Indiana University, Purdue University, and Bowling Green State University. He has taught - at the undergraduate, graduate, and doctoral levels – dozens of courses on statistics, optimization modeling, project management, quality assurance, simulation modeling, and supply chain management.
Dr. Loucks’ consulting experience is in a wide range of fields, including government, manufacturing, financial services, and education. From this experience he has gained a sense of which analytical tools are more useful and what challenges arise in both using the tools and implementing the solutions found. John has a passion for sharing his theoretical and practical knowledge gained over 35+ years and helping students gain the analytical skills needed to advance their careers. He’s known for explaining concepts and algorithms is a clear manner and providing example applications to demonstrate their relevance.
Dr. Yong Shin Park
Associate Professor
Dr. Chen Xu
Assistant Professor
Tuition & Financing
At $36,000*, a Master of Science in Business Analytics degree is a smart investment. The skills acquired in this program will position you for a career in business analytics. Tuition includes all course fees but does not include books, comprehensive fees, or other course materials. Once accepted to the program, you are required to submit a $500 non-refundable tuition deposit. Deposits are applied toward tuition and secure your place in the upcoming class.
*Tuition is subject to change at the discretion of the St. Edward’s University Board of Trustees.
Financial Aid
The St. Edward’s University Financial Aid office provides information about financial aid opportunities available to graduate students.
Please visit our Financial Aid page or call us at (512) 387-3110 if you are interested in additional details.
SEU’s online MSBA is accessible to students with all academic backgrounds. A background in advanced math or computer programming is not required. You can learn something new, and build on what you know, without intimidation. For application dates and to submit an application, please go to the Graduate Application Page.