M.S. in Business Analytics


Contact us today to learn more about our program.



students in analytics class

The emergence of advanced technologies for capturing and analyzing data provides unprecedented opportunities for those with business analytics expertise that spans all industries and organizations. Combine your business skills and analytical acumen to become a professional with a Master of Science degree in Business Analytics (MSBA). By earning a master’s in business analytics, you will increase your viability in a competitive market for sought-after analytics professionals. Watch the MSBA Webinar. 


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Program Features


The MSBA Program is offered in the Fall and Spring and is available both 100% online and in-person - no GRE/GMAT required!


The language of business today is dependent on information and data management. The Kent State University MSBA program provides you with a holistic knowledge of analytics that balances the technologies, analytical and business expertise you need to be able to glean useful information from data and make strategic business decisions.

With a KSU MSBA, you will gain the technical, analytical, communication, decision-making and leadership skills you need to be a successful business analyst. The in-person and online curriculum includes integrative capstone analysis projects, as well as an internship option for more professional development through our on-site Career Services Office dedicated to business students. With our STEM designation, international F-1 students qualify for OPT (Optional Practical Training), which helps them to acquire additional career experience while at Kent State.

Core Competencies

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 Extraction
  • 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: R and Python

  • Linear Programming 

  • Integer Programming 

  • Goal Programming 

  • Simulated Annealing 

  • Network Models 

  • Genetic Algorithms/ Programming 

Data Preparation General Purpose Programming: R and Python

  • 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/Microsoft Power BI

  • 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 
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

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
Quantitative Algorithms

Quantitative Algorithms

  • Linear Programming 
  • Duality in Linear Programming 
  • Integer Programming
  • Goal Programming 
  • Simulated Annealing 
  • Network Models 
  • Genetic Algorithms/ Programming 
Business Acumen

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 occurs during the fall and spring and is highly competitive. Applying early is highly recommended as applications are reviewed on a rolling basis throughout the admission cycle. In addition to the online application form and fee, you must submit:

  • Accredited college/university official transcripts
  • Resume
  • Two letters of recommendation
  • Essay of goals and objectives
  • English score for international applicants (500 TOEFL paper-based, 79 TOEFL IBT, 6.5 IELTS, 110 Duolingo)
  • GMAT/GRE score is not required for this program 

We look forward to seeing you in our master’s in business analytics program, so be sure your application materials fully meet the above requirements.


Due to its very multi-disciplinary nature, our MSBA accepts students with different academic backgrounds, including students with business, engineering, computer science, and other science disciplines. Some previous exposure to information/computer systems, applied statistics/math, and business, through coursework and/or work experience is expected.

Tuition and Financial Assistance

  Ohio Residents Non-Ohio Residents
Below 11 credit hours $549.90 per credit hour $1,024.90 per credit hour
11-18 credit hours $6,036 $11,261.40

Learn More | KSU Tuition & Fees


Scholarships are available. These awards are very competitive and are awarded on the basis of academic merit


The MSBA program at Kent State University is taught by full-time faculty with analytical and industry experience.


Director, MSBA Program 








Contact Us


If you would like to know more about our MS in business analytics, or want to get a better sense of what goes into our data analytics courses, be sure to reach out to us today.


Mason McLeod Mason McLeod
Admissions Coordinator







 Rouzbeh Razavi, Ph.D.
 Director, MSBA Program





 Roberto Chavez
 Graduate Programs Director