M.S. in Business Analytics Online

Earn an EMBA in 19 Months

An EMBA program tailored to meet the needs of professionals ready to move up the corporate ladder.

An EMBA program tailored to meet the needs of professionals ready to move up the corporate ladder


Contact us today to learn more about our program.


With the emergence of advanced technologies for capturing, preparing and analyzing data, come a wealth of unparalleled opportunities for those with business analytics expertise. MSBA Ranked #12 best in the nation by Fortune

When you pursue a Master of Science degree in Business Analytics (MSBA) from Kent State University, you will combine your business skills and analytical expertise. With a master’s in business analytics, you will increase your viability in a competitive market for sought-after analytics professionals that are needed in all industries and organizations. 



rouzbeh razavi

Rouzbeh Razavi, Ph.D.

Director, MSBA Program

Listen to Dr. Rouzbeh Razavi’s podcast about the MSBA program at Kent State.

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Check out the MSBA Blog to learn more about the program


Online MSBA Program Features

Receive Your M.S. in Business Analytics Online

Kent State’s MSBA Program is available in person, as well as 100% online. Our online business analytics degree offers the same standard of teaching and learning excellence that is delivered on campus, but with the flexibility of online courses.

Our online MSBA was designed with the busy working professional in mind. Developed by the same industry-leading faculty who teach on campus, our online program prepares you as a leader in the business analytics field.


Today’s business world is dependent on information and data management. Kent State University’s online MSBA program provides graduates with a holistic knowledge of analytics that balances the analytical methods, technologies and business knowledge needed to collect valuable information from data and make strategic business decisions based on that data.

With our STEM designation, international F-1 students qualify for OPT (Optional Practical Training), granting them the ability to acquire additional and relevant career experience while at Kent State.


When you receive your MSBA from Kent State University, you learn the communication, technical, analytical, decision-making and leadership skills needed to be a successful business analyst. The online MSBA curriculum is comprised of integrative capstone analysis projects, as well as an internship option for additional professional development.

Learn More About Our MSBA Online Program

If you would like to know more about our online master’s in business analytics degree or our data analytics courses, be sure to contact us today.

Core Competencies

By earning your master’s in business analytics, you will be increasing your viability in a competitive market because recruiters regularly seek out those with an MSBA degree.

The skills you will acquire as part of our MSBA program can be put to use in everything from small businesses and start-ups to Fortune 100 companies, so you will just need to determine your best fit. Additionally, research from the McKinsey Global Institute and the U.S. Bureau of Labor Statistics shows that talent for the field of data analytics is sorely needed, so you can be confident in knowing that the education you will receive through our data analytics courses will open numerous doors for your career.

Data Mining / Machine Learning

Data Mining / Machine Learning

Principles of Machine Learning and Data Modeling

  • Data Structures and Types of Variables
  • Supervised vs. Unsupervised Machine Learning Modeling
  • Data Preparation Techniques
  • Feature Engineering
  • Evaluation of Machine Learning Models
  • Optimizing Machine Learning Models
  • Ensemble Learning
  • Common Mistakes in Modeling

Regression Modeling

  • Concepts and Definitions
  • Performance Metrics
  • Linear Regression
  • Generalized Linear Models (GLM)

Classification Modeling

  • Concepts and Definitions
  • Performance Metrics
  • Logistic Regression
  • k-Nearest Neighbor (k-NN)
  • Naïve Bayes  
  • Decision Trees (applied to Regression as well)
  • Random Forrest (applied to Regression as well)
  • Gradient Boosted Machines (applied to Regression as well)
  • Support Vector Machines (applied to Regression as well)
  • Neural Networks (applied to Regression as well)

Recommendation Systems

  • Concepts and Definitions
  • Performance Metrics
  • Apriori algorithm for association data mining

Time Series Analysis 

  • Concepts and Definitions
  • Performance Metrics
  • Stationarity, casuality and invertibility
  • Autoregressive Integrated Moving Average (ARIMA) 

Graph Analytics

  • Concepts and Definitions
  • Centrality and Connectivity Measures
  • Application to Social Network Analysis

Text Analytics 

  • Concepts and Definitions
  • Feature Extration
  • Topic Modeling
  • Sentiments Analysis
Programming and Software Tools

Data Mining, Machine Learning and Quantitative Programming: R

Implementation of the following Data Mining/ Machine Learning methods:

  • Linear Regression 

  • Generalized Linear Models  

  • Logistic Regression 

  • Decision Trees 

  • Random Forrest 

  • Gradient Boosted Machines 

  • Support Vector Machines 

  • Neural Networks 

Implementation of the following Quantitative methods:

  • Linear Programming 

  • Integer Programming 

  • Goal Programming 

  • Simulated Annealing 

  • Network Models 

  • Genetic Algorithms/ Programming 

Open Source Data Mining Tool: WEKA 

Implementation of the following Data Mining/ Machine Learning methods:

  • k-Nearest Neighbor (k-NN) 
  • Naïve Bayes  
  • Decision Trees 
  • Bagged/Boosted Trees 
  • Association Mining 

Data Preparation General Purpose Programming: R

  • Calculating Various Statistics and Math Calculations 
  • Calculating Probability Values 
  • Data Input/ Export 
  • Data Cleansing 
  • Data Wrangling and Data Subsetting 
  • Feature Engineering 
  • Applying summarization and Aggregate functions 

Database: SQL 

  • Principals of Database Design 
  • Using SQL to Create, Update and Delete Tables 
  • Using SQL to Select a subset of Data 
  • Using SQL to Join Tables 
  • Using SQL to perform various Aggregate Functions 

Visualization: R/ Tableau

  • Using R ‘ggplot’ for explanatory analysis and communicating the insights 
  • Using R ‘Shiny’ for interactive visualization and dash boarding 
  • Using Tableau for explanatory analysis and communicating the insights

Software Repository and Development Platforms: Github/Git

  • Creating a new repository 
  • Fork and Push changes to a repository 
  • Clone a public project 
  • Send a pull request/ Merge changes from a pull request 

Big Data and High performance Computing: Spark, Hadoop, AWS, Azure, MLlib, R

  • Spark and Big Data Ecosystem 
  • Using Spark's MLlib for Machine Learning
  • Scale up Spark jobs using Amazon Web Services
  • Using R in Azure Machine Learning Studio
  • Parallel computing using R 
Applied Probability and Statistics

Applied Probability and Statistics


  • Distributing Functions
  • Normal Distribution 
  • Uncertainty and Confidence Intervals  
  • Conditional Probabilities 
  • Bayesian Probability 
  • Information Entropy 


  • Measures of Central Tendencies 
  • Measures of Dispersion
  • Measures of Skewness 
  • Measures of Dependence 
  • Statistical Significance 
  • A/B Testing 
Databases and Data Processing

Databases and Data Processing 

Relational Databases 

  • Concepts and Definitions 
  • Entity-Relationship Diagrams 
  • Structured Query Language (SQL)
  • Normalization, Transaction management and Concurrency Control 
  • SQL as an Analytical Tool 
  • Intro to NoSQL Databases and Applications 

Big Data Platforms

  • Big Data Paradigms (e.g. MapReduce) 
  • Big Data Platforms (e.g. Hadoop) 
  • Big Data Extraction/Integration 
  • Big Graph Processing 
  • Big Data Stream Techniques and Algorithms 
Quantitative Algorithms


  • Linear Programming 
  • Duality in Linear Programming 
  • Integer Programming
  • Goal Programming 
  • Simulated Annealing 
  • Network Models 
  • Genetic Algorithms/ Programming 
Business Acumen
  • Practical Case Studies Based on Real-World Data from Different Industries 
  • Formulation of Business Problems to Solve Using Analytics Group Projects 
  • Data Collection and Communication of Findings 
  • Operationalizing Analytical Models in Practice 
  • Common Mistakes in Analytical Modeling 

Curriculum Structure


Learn More

Admission Requirements

Admission to the online MSBA program only occurs during the fall semester and is highly competitive. We strongly recommend applying early.

All applicants should have some prior exposure to information/computer systems, applied statistics/math, and business, whether through coursework and/or work experience.

In addition to the online application form and fee, all applicants must submit the following:

  • Official transcripts from an accredited college or university
  • GMAT/GRE score (this requirement is currently being waived for Fall 2021 and Fall 2022 applications)
  • Résumé
  • Three letters of recommendation
  • Essay of goals and objectives
  • TOEFL score (international applicants)

Please ensure your application materials fully meet the requirements above. We look forward to you joining our master’s in business analytics online program.

Tuition and Financial Assistance

As of Fall 2021, tuition information for the master’s in business analytics online program are as follows:





$536 $546


$12 $12


Online MSBA Faculty

Kent State University’s online MSBA program is taught by full-time faculty with a wealth of analytical, industry experience.


Director, MSBA Program 





Contact Us


 Alli Gribben
 Graduate Programs Admissions Coordinator







 Rouzbeh Razavi, Ph.D.
 Director, MSBA Program





 Roberto Chavez
 Graduate Programs Director