M.S. in Business Analytics (Online)

When pursuing your online business analytics degree at Kent State University, you are given the flexibility to complete your studies on your own schedule. The online MSBA program’s required 30 credit hours can be completed in as few as 12 months, or even over multiple years for those wanting to pace themselves.

Learn more from our informational guide to pursuing your M.S. in Business Analytics online at Kent State University today.

Kent State’s online MSBA program is offered in the fall and spring semesters. 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. Best yet, no GRE/GMAT is required! 

Plus, our online business analytics degree was designed with the busy working professional in mind. Developed by the same industry-leading faculty who teach in person, 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, technological, and business knowledge needed to collect valuable information from data and make strategic business decisions based on that data.

With a Kent State online business analytics degree, you will gain the technical, analytical, communication, decision-making, and leadership skills you need to be a successful business analyst. The MSBA 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), granting them the ability to acquire additional and relevant career experience while at Kent State.

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, causality, 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

Data Mining, Machine Learning and Quantitative Programming: R and Python

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 

Probability:

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

Statistics: 

• Measures of Central Tendencies 
• Measures of Dispersion
• Measures of Skewness 
• Measures of Dependence 
• Statistical Significance 
• A/B Testing 

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 

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

• 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