Program Description
The Online MS in Business Analytics provides students the managerial and technical skills for data-driven decision making in an organization. Students will learn how to analyze data and apply quantitative models to understanding business, organizational, and societal issues. The program emphasizes the ability to formulate insightful questions using data, provide actionable solutions to business and economic problems, and to interpret analytical models to improve business and economic decision making. Graduates of this program will have technical, quantitative, and business skills for today’s increasingly data-driven professions.
This program is delivered in a fully online format.
Program Director
Dr. Yogesh Uppal
Williamson Hall 3377
(330) 941-3494
yuppal@ysu.edu
Admission Requirements
Regular Admission, applicants must meet the criteria of one of the following admission pathways:
- Earned an undergraduate degree in business, economics, or STEM[1] with a 3.0+ GPA. No full-time professional work experience or standardized test score required.
- Earned an undergraduate degree in a discipline other than business, economics, or STEM[1] with a 2.7+ GPA and a strong quantitative ability as demonstrated in professional experience [2], prior coursework, professional certifications [3] or standardized test scores
- Earned a graduate or terminal degree (e.g., PhD, MD, or JD) in any field. No work experience or standardized test required.
[1] STEM is an acronym that refers to teaching and learning in the fields of science, technology, engineering, and mathematics. For a complete list of specific degree programs that meet the STEM criteria, refer to the DHS STEM Designated Degree Program List. If a specific degree program (the CIP Code Title) is not included in this list, the degree program does not meet the STEM admission criteria.
[2] Relevant work experience is determined by using an applicant's chronological resume and any requested supporting documents. Work experience is not simply a count of the years of employment but strongly considers the relevance of the full-time experience as it relates to the nature of the program. This is typically demonstrated through a history of full-time positions where an applicant has documentable experience of working on budgets, financial forecasting, data analysis, performance metrics, market research, marketing analytics, process optimizations and/or project management. For strong applicants, the work experience qualification is supported by a career progression evidenced by increased roles, responsibilities, accomplishments and/or formal promotions. Please note: The chronological resume may include non-professional positions, part-time (i.e., less than 40 hours per week) professional positions, volunteerism or internships, provided they demonstrate experience as it relates to the nature of the program.
[3] List of professional certifications (in alphabetical order): CFA (Charted Financial Analyst), CFP (Certified Financial Planner), CMA (Certified Management Accountant), CPA (Certified Public Accountant), CPIM (Certified in Planning and Inventory Management), CSCP (Certified Supply Chain Professional), CSM (Certified ScrumMaster), Lean Black Belt (certified by IISE), Lean Six Sigma Black Belt (certified by IISE), Six Sigma Black Belt (certified by either ASQ or IISE).
Degree Requirements
Students must complete 30 semester hours of graduate credit with a grade point average of 3.0 or higher for the M.S. in Business Analytics.
| COURSE | TITLE | S.H. |
|---|---|---|
| Required Courses | ||
| MSBA 6930 | Business Analytics | 3 |
| MSBA 6931 | Business Data Visualization | 3 |
| MSBA 6932 | AI for Business Analytics | 3 |
| MSBA 6940 | Data Analytics and Data Management | 3 |
| MSBA 6939 | Applied Investment Analysis | 3 |
| OMBA 6955 | Marketing Strategy | 3 |
| MSBA 6915 | Healthcare Analytics | 3 |
| MSBA 6976 | Predictive Analytics for Business Decision-Making | 3 |
| OMBA 6921 | Industrial Economics | 3 |
| MSBA 6998 | Business Analytics Project | 3 |
| Total Semester Hours | 30 | |
Learning Outcomes
Graduates of the Master of Science in Business Analytics should be able to do the following:
1. Describe Relevant Statistical Concepts: Demonstrate knowledge of key statistical principles and methods used in business analytics to summarize, analyze, and draw conclusions from business data.
2. Proficiency in Analytical Tools and Technologies: Exhibit proficiency in industry-standard tools and technologies for data analysis and decision support.
3. Develop Insightful, Data-Driven Questions: Demonstrate the ability to formulate meaningful questions that address business challenges using data.
4. Data Analysis and Interpretation for Business Insight: Apply data tools and methods to uncover patterns, trends, and implications that inform business decisions.
5. Communicate Analytical Findings: Effectively communicate data-driven insights and recommendations to technical and non-technical stakeholders.
Tomi P. Ovaska, Ph.D., Professor
Public finance; comparative economic systems; entrepreneurship; international trade; behavioral economics
Joseph Palardy, Ph.D., Professor
Macroeconomics; time series econometrics
Albert J. Sumell, Ph.D., Professor
Urban, housing, and environmental economics
Yogesh Uppal, Ph.D., Professor, Director
Applied microeconomics; applied econometrics; public economics; political economy; development economics
Yaqin Wang, Ph.D., Professor
Futures markets; behavioral economics
MSBA 6915 Healthcare Analytics 3 s.h.
In this course we will learn skills necessary to analyze and interpret healthcare data to improve decision-making, patient outcomes, and overall healthcare system performance. This field has gained significant importance in recent years due to the increasing availability of healthcare data and the need for evidence-based decision-making in the industry.
MSBA 6930 Business Analytics 3 s.h.
Explore business analytics through a comprehensive approach that develops the skills and tools needed to transform data into actionable insights for strategic decision-making. Designed for professionals aiming to excel in a data-driven economy, the content bridges the gap between business strategy and analytical methods, ensuring practical application in real-world scenarios.
Cross-Listed: OMBA 6930.
MSBA 6931 Business Data Visualization 3 s.h.
Develop the skills to transform raw data into compelling visual narratives, creating dynamic dashboards and effective visualizations that drive data-informed decision-making. Learn key principles of data visualization, including best practices for chart selection, color theory, and dashboard design, while exploring advanced functionalities such as calculated fields, parameters, and interactivity to enhance user engagement and insight communication.
Cross-Listed: OMBA 6931.
MSBA 6932 AI for Business Analytics 3 s.h.
This course explores the use of AI tools to simplify and enhance the data analytics process. Students will learn to integrate AI for data cleaning, analysis, visualization, and automation. The course emphasizing how AI can assist in writing code, handling unstructured data, creating reports, and automating workflows. By the end, students will design a fully functional, AI-enhanced analytics pipeline.
Cross-Listed: OMBA 6932.
MSBA 6939 Applied Investment Analysis 3 s.h.
This course introduces graduate students to core investment principles with a strong emphasis on applied, data-driven analysis. Students learn to evaluate financial markets, analyze securities, construct portfolios, and make investment decisions using Excel, Python, and Tableau. The course integrates financial statement analysis, portfolio theory, risk/return models, and equity/fixed-income valuation with hands-on data applications.
MSBA 6940 Data Analytics and Data Management 3 s.h.
Course emphasis is on knowledge and skills required by accountants and managers to collect, manage, analyze extremely large volumes of data in various formats from numerous sources. Focus will be given to results that management of data brings to an organization. It will cover a broad spectrum of topics chosen from the following: predictive analytics, enterprise architecture, security, knowledge through data discovery, data visualization, ethics data quality, advanced data modeling. It will include hands-on use of available software found in industry practices, with an emphasis on spreadsheets.
MSBA 6976 Predictive Analytics for Business Decision-Making 3 s.h.
An applied course in predictive analytics designed for aspiring data analysts. Students develop hands-on skills in regression modeling, forecasting, and data-driven decision-making. The course emphasizes practical applications using AI-assisted coding techniques, focusing on model building, validation, interpretation, and communication of results to non-technical stakeholders. Topics include linear regression, logistic regression, model selection and validation, cross-validation, and handling common data issues.
Prereq.: BUS 6930 and BUS 6932.
MSBA 6998 Business Analytics Project 3 s.h.
This course serves as the culminating experience in the business analytics program, allowing students to apply the knowledge and skills gained throughout their coursework. Students analyze real-world business problems using data-driven approaches, advanced analytical tools, and visualization techniques. Emphasis is placed on integrating technical, managerial, and communication skills to deliver actionable insights and strategic recommendations to stakeholders.
Prereq.: BUS 6930 OR MSBA 6930; BUS 6932 OR MSBA 6932.
Prereq. or Coreq.: ECON 6976 OR MSBA 6976.