Online Master of Science in Business Analytics (MSBA)

What is Business Analytics?

Business analytics is the foundational course of the MSBA program, introducing the vocabulary, frameworks, and analytical mindset that every subsequent course builds on.

The online Master of Science in Business Analytics at Youngstown State University is built on the idea that data creates value only when it's connected to a clear business question and a strategic decision. This course makes that connection by covering the fundamentals of data collection, preparation, and interpretation before introducing the probability concepts and diagnostic and predictive methods that recur throughout the degree. Because this course is typically taken early in the program, the analytical vocabulary and business-framing skills developed here carry forward into every specialized course that follows, from AI for Business Analytics to Predictive Analytics, and into career paths such as Business Intelligence Analyst, Data Analyst, and Business Analytics Manager.

​Upon successful completion of the course, the student will be able to:

• Define business analytics and its role in the business analysis process.

• Apply fundamentals of data concepts, collection, and preparation.

• Describe data effectively to identify essential business insights.

• Identify basic concepts of probability and probability distributions and their application to various business contexts.

• Use diagnostic methods for initial insight contextualization.

• Explain the fundamentals of predictive analytics and its applications.

How Is AI Used in Business Analytics?

AI for business analytics examines how artificial intelligence tools are reshaping the analytics workflow, from the first prompt to a finished, automated pipeline.

Employers increasingly expect analytics professionals to work alongside AI tools rather than around them, and this course builds that fluency directly into the MSBA curriculum. You'll learn to translate business questions into AI-executable tasks, apply AI across the full data pipeline, and critically evaluate AI-generated outputs rather than accepting them at face value. This hands-on experience with agentic AI and workflow automation is directly relevant to roles like data analyst, business intelligence analyst, and director of analytics, where the ability to responsibly deploy AI tools is becoming a core differentiator rather than a specialized skill.

Upon successful completion of the course, the student will be able to:

• Construct effective prompts that translate business questions into AI-executable analytical tasks.

• Apply AI tools to extract, clean, and transform data across the Bronze-Silver-Gold pipeline architecture.

• Evaluate and verify AI-generated code and analytical outputs using appropriate validation strategies.

• Design automated analytics workflows using agentic AI and orchestration tools.

• Communicate AI-enhanced analytical findings and pipeline architecture to business stakeholders.

What is Data Analytics and Data Management?

Data analytics and data management is the technical backbone of the msba program, giving you the database architecture and data-quality skills needed to work confidently with large, complex datasets.

Before data can generate insight, it has to be collected, structured, and validated, and this course builds the practical skills that make that possible. You'll work with different database structures and query techniques, apply data normalization and integrity principles, and use tools like Tableau and SPSS to visualize and interpret large-scale datasets. This grounding in data infrastructure and quality supports every analytical course that follows in the program, and is especially relevant to graduates pursuing roles such as data analyst, business intelligence analyst, or director of analytics, where trustworthy data is the prerequisite for every other analytical decision.

Upon successful completion of the course, the student will be able to:

• Compare various types of datasets, including cross-sectional, time-series, and panel datasets, and their business applications.

• Explain various types of database structures, including hierarchical, network, relational, and object-oriented databases, and their applications.

• Apply data normalization methods and techniques.

• Examine standard data queries, including common commands, clauses, operators, aggregate functions, and string functions, to determine whether the retrieved dataset is relevant and complete.

• Interpret merged data from different sources for information necessary in financial and operational analysis and decision-making.

• Analyze a relational database structure to determine whether it applies data-integrity rules, uses a data dictionary, and normalizes the data.

• Create interactive data visualizations that provide clear insights into associations, relationships, outliers, and other patterns in datasets.

• Apply data-mining and analytic techniques to identify outliers and risk factors in underlying data.

What is Applied Investment Analysis?

Applied investment analysis puts core investment theory into practice, using real data and financial tools to evaluate markets, securities, and portfolios.

This course connects the analytical skills built earlier in the MSBA program to one of business analytics' most quantitative applications: investment decision-making. You'll compute risk and return metrics, apply portfolio theory and valuation models, and use Excel, Python, and Tableau to support investment recommendations, while also learning to audit AI-assisted analysis for accuracy and integrity. Graduates who complete this course build a skill set directly applicable to roles such as financial analyst and financial analytics manager, where translating quantitative models into clear investment guidance is essential.

Upon successful completion of the course, the student will be able to:

• Formulate data-driven investment questions that align investor objectives with appropriate asset classes, macroeconomic data, and analytical methods.

• Compute key investment metrics for asset and portfolio evaluation, including returns, volatility, risk-adjusted performance, beta, alpha, bond yields, and intrinsic values.

• Evaluate securities and portfolios through core investment frameworks, including Modern Portfolio Theory, the Capital Asset Pricing Model, market efficiency, and valuation models.

• Audit AI-assisted analytical workflows for prompt documentation, data integrity, model logic, and the validity of conclusions.

• Communicate fundamental and quantitative evidence through dashboards, memos, and presentations to support investment recommendations and portfolio rebalancing decisions.

What is Healthcare Analytics?

Healthcare analytics applies the program's core data skills to one of the most consequential and fastest-growing areas of analytics work: healthcare decision-making.

This course combines economic reasoning with statistical methods to help you evaluate healthcare costs, quality, and outcomes using real-world data sources. You'll apply descriptive statistics, regression, and hypothesis testing to questions about patient behavior, provider incentives, and insurance design, building the applied, industry-specific expertise that distinguishes a Business Analytics degree from a generalist data credential. This coursework is directly relevant to graduates pursuing roles such as healthcare data consultant, where economic insight and data fluency both matter.

Upon successful completion of the course, the student will be able to:

• Apply core economic concepts, including incentives, opportunity cost, supply and demand, market structure, moral hazard, adverse selection, and externalities, to analyze healthcare markets, provider behavior, insurance design, and patient decision-making.

• Apply healthcare data-management skills, including data acquisition, organization, and cleaning, using common data sources.

• Employ statistical methods used in healthcare economics, including descriptive analysis, regression techniques, hypothesis testing, and data visualization, to evaluate healthcare utilization, costs, quality, outcomes, and policy effects.

• Analyze real-world healthcare issues through the application of economic theory and data to assess impacts on costs, access, efficiency, and health outcomes.

What is Predictive Analytics?

Predictive analytics teaches you to distinguish signal from noise, and correlation from causation, using the statistical and AI tools that drive modern business forecasting.

Predicting outcomes with confidence and explaining why a model's results are trustworthy is one of the most valued skills a business analytics graduate can bring to an organization. This course builds that capability directly, covering regression techniques, causal inference methods, and the critical evaluation skills needed to use AI-generated analysis responsibly. Graduates apply these forecasting and communication skills in roles such as business analytics manager and director of analytics, where predictive insight informs high-stakes strategic decisions.

Upon successful completion of the course, the student will be able to:

• Reason with data by applying statistical methods to draw valid inferences from sample data to populations.

• Distinguish correlation from causation by critically evaluating when regression results indicate causal relationships versus mere correlations.

• Apply regression techniques by interpreting linear and logistic regression models for business prediction problems.

• Establish causal inference by employing experimental and quasi-experimental methods to identify causal relationships from observational data.

• Leverage AI tools by critically evaluating AI-generated analytical outputs for accuracy.

• Communicate analytical findings by presenting predictive analytics results clearly to inform business strategy decisions.

What is Business Data Visualization?

Business data visualization develops the design and communication skills needed to turn raw analysis into insights that stakeholders can act on quickly.

Even the strongest analysis loses value if decision-makers can't understand it and act on it, and this course closes that gap. You'll learn to build interactive dashboards in Tableau, apply best practices in chart selection and color theory, and use calculated fields and parameters to surface the insights that matter most. As the program's elective course, this skill set rounds out the MSBA curriculum with the visual communication ability that roles like business intelligence analyst, marketing analyst, and operations analyst rely on daily to translate data into decisions.

Upon successful completion of the course, the student will be able to:

• Define data-visualization concepts and their role in analytics.

• Create data connections in Tableau and navigate the Tableau user interface and layout.

• Build basic visualizations in Tableau.

• Examine key data-management concepts in Tableau.

• Leverage Tableau functions for analytics.

• Identify key business recommendations through dashboards.

• Apply important visualization techniques effectively to solve business problems.