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Thank you for your interest in the Master of Science in Business Analytics program at ����������Ƶ. A master’s 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 Business Analytics 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 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.

– Yongshin Park, PhD

   Director, MS in Business Analytics

Learning Goals:
Our Business Analytics program prepares students with knowledge and skills that are in high demand in the workplace. You’ll learn to analyze historical data for trends and patterns, diagnose problems, forecast and predict future outcomes, and help model scenarios to make data-driven decisions. When you graduate, you’ll have the ability to:

• Translate a problem or opportunity described by a layperson to a technical model formulation. 
• Identifying the appropriate analytical tool(s) for a particular objective.      
• Execute the most-used methods for descriptive, diagnostic, predictive and prescriptive business analysis.  
• Apply analytics to a variety of business functions at different levels for decision-making.
• Contribute to the decision-making process by producing clear courses of action for consideration by managers.
• Work fluently in Tableau, Python, R, Power BI, SQL and Arena software.

Outcomes:
An individual graduating from the program without relevant work experience is expected to immediately qualify for an entry-level position such as a 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 Business Analytics program makes our graduates strong candidates for advancement to high-level administrative positions with titles such as business analytics director and even chief operations officer.

The online MS in Business Analytics 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 Capstone final project. 

Core Courses:

• Introduction to Business Analytics and R – BANA 6310
• Data Summarization and Visualization – BANA 6312
• Python for Business Analytics – BANA 6320
• Big Data and Database Management – BANA 6330
• Inferential Statistics – BANA 6332
• Predictive Analytics – BANA 6340
• Simulation Modeling – BANA 6350
• Artificial Intelligence and Machine Learning – BANA 6352
• Optimization Modeling – BANA 6360
• Business Analytics Practicum – BANA 6370

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Detailed Course Descriptions:

Introduction to Business Analytics and R – BANA 6310

This course presents an introduction to the concepts of data analysis and the tools that are used to perform daily functions. It is broken into two major components. 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

Data Summarization and Visualization – BANA 6312

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

Python for Business Analytics – BANA 6320

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 6332

Big Data and Database Management – BANA 6330

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 and data security, and ethical and legal issues surrounding the use of data in our modern society. 

Prerequisite: None

Inferential Statistics – BANA 6332

This course focuses on the understanding and application of inferential statistics in the decision-making process. Students develop the statistical foundation that underpins 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: None

Predictive Analytics – BANA 6340

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 6310

Simulation Modeling – BANA 6350

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 into 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: None

Artificial Intelligence and Machine Learning – BANA 6352

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: None

Optimization Modeling – BANA 6360

In this course, students learn how to use prescriptive analytics methods classified as optimization approaches. The forms of mathematical programming methods covered include linear and nonlinear, integer and noninteger, deterministic and stochastic, as well as single- and multiple-objective. In applying prescriptive analytics methods to problems in a variety of business functions, students are exposed to all the steps of decision modeling, from problem formulation to the identification of alternative solutions and the sensitivity of the solutions to the assumptions made. 

Prerequisite: None

Business Analytics Practicum – BANA 6370

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: Final term or w/Director’s approval

Technologies:

Students will gain experience with the following 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)

Our faculty in the MS in Business Analytics program work to strike the ideal balance between theoretical and practical skills. They 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. 

Fatemeh Firouzi, PhD

Assistant Professor of Business Analytics

Fatemeh Firouzi earned her PhD 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 experience 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.

Omid Jadidi, PhD

Assistant Professor of Operations Management

Omid Jadidi 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. Jadidi’s research has been published in leading journals related to the courses he teaches. He also has multiple years of industry experience working as a project controller, industrial engineer and material planning manager.

Akhil Jonnalagadda, PhD

Adjunct Professor

Sri “Akhil” Jonnalagadda is a distinguished professional with a robust educational foundation and extensive expertise in quantitative finance, economics education and research. He holds an MA in Economics and a BA in Economics, both earned from The University of Texas at Austin. Over the past four years, Jonnalagadda has been an integral member of the faculty at St. Edwards University, where he has excelled in teaching undergraduate courses in microeconomics, macroeconomics and healthcare economics, as well as graduate-level courses covering Python, R, SQL and Statistics.

In addition to his academic role, Jonnalagadda has garnered significant experience in the financial sector and consulting industry. His professional tenure includes roles at prominent financial institutions and consulting firms, where he has demonstrated proficiency in econometric modeling, financial risk analysis and macroeconomic forecasting. Furthermore, he has made noteworthy contributions to research, with publications and involvement in projects spanning critical illness surveillance, LNG price dynamics and advancements in treatment strategies for cystic fibrosis. 

Teddy Kim, MPS

Adjunct Professor

Teddy Kim possesses a solid academic background and expertise in healthcare data analysis, biomedical informatics, and clinical research. He earned his Master of Professional Science degree in Biomedical and Health Informatics from the University of North Carolina at Chapel Hill, along with a Bachelor’s degree in Exercise Science from the University of Texas at Austin. He is instructing graduate-level courses that focus on using tools such as Tableau and Power BI, which improve data analytics by offering intuitive visualizations, helping users swiftly understand complex data sets, and facilitating more informed decision-making. Apart from his academic position, Teddy utilized his expertise in data discovery, transformation, harmonization, and modeling to facilitate the effective integration of diverse datasets within the biotechnology industry. He implemented new procedures and quality checks to minimize discrepancies in biomedical data, developed robust data models, and enhanced data quality. In addition, he conducted analyses of hospital readmission to identify patterns and trends, aiding healthcare providers in optimizing resource allocation and formulating strategies to reduce unnecessary hospitalizations.

John Loucks, PhD

Professor

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 the University of New Mexico. In addition, he has attained ASQ’s Certified Quality Engineer and APICS’s Certified Production and Inventory Manager credentials. 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.

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. Loucks 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 is known for clearly explaining concepts and algorithms and providing example applications to demonstrate their relevance.

Yong Shin Park, PhD

Associate Professor of Operations Management

Yong Shin Park teaches Big Data, Business Intelligence and Analytics, Data Summarization and Visualization, Simulation, Operations Management, and Business Statistics. Through his dynamic teaching style and innovative curriculum, he inspires students to explore the complexities of operations management and equips them with practical skills for success in the field. 

Park’s research is characterized by a methodology that integrates quantitative modeling of operations management problems with a focus on sustainability. His work addresses pressing environmental challenges, such as optimizing bio-energy supply chain networks and developing a sustainability index of transportation systems and ports. Park’s research demonstrates his commitment to advancing academic knowledge and practical solutions. His work has been published in esteemed journals, including OMEGA, Transportation Research Part D: Transport and Environment, Journal of Cleaner Production, Journal of the Operational Research Society, and International Journal of Logistics Research and Applications, among others. 

Chen Xu, PhD

Assistant Professor of Economics

Chen Xu holds a PhD degree in Economics and a bachelor's degree in Mathematics, both from the University of Oklahoma. Xu has expertise in applied microeconomics, with a particular focus on labor economics, development economics and applied econometrics. His rigorous quantitative approach to research enables him to employ advanced econometric techniques, leading to impactful findings on economic issues.

In addition to research, Xu is a passionate educator, having taught a wide range of economics and data analysis courses at both undergraduate and graduate levels. He has designed and delivered courses on principles of economics, intermediate economics, labor economics, Chinese economy, econometrics and predictive analytics, as well as specialized courses for MBA programs. His teaching philosophy emphasizes the application of knowledge to real-world problems, preparing students to tackle contemporary economic and business challenges with analytical precision and informed insights.

At $36,000, the online Master of Science in Business Analytics degree is a smart investment. The skills acquired in this program 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 ����������Ƶ Board of Trustees.

Financial Aid

The ����������Ƶ 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.

To apply for the program, students are required to have a bachelor’s degree from an accredited university and complete the application process. For application dates and to submit an application, please go to the Graduate Application Page. 

Need more information? Please contact an Enrollment Counselor at (512) 326-7501.