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MS business analytics online

M.S. in Business Analytics

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The emergence of advanced technologies for capturing, preparing 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.

Rouzbeh Razavi, Ph.D.

Director, MSBA Program
rrazavi@kent.edu

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

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Watch a recording of the 2020 Graduate Programs Week Alumni Panel here. Learn about"Career Resilience in the Midst of COVID-19" from former graduate business students who shared insights and tips on navigating challenging situations in the workplace.

Program Features

The MSBA Program is available both 100% online and in-person

THREE-FOCI STEM PROGRAM

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 methods and business expertise you need to be able to glean useful information from data and make strategic business decisions.

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.

OUR UNIVERSITY ENVIRONMENT

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.

LEARN MORE ABOUT OUR MSBA PROGRAM

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.

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

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

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

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

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

Admission occurs during fall semester only and is highly competitive. Applying early is highly recommended.

In addition to the online application form and fee, you must submit:

Accredited college/university official transcripts
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)

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.

REQUIREMENTS

Applicants should have some previous exposure to information/computer systems, applied statistics/math, and business, through coursework and/or work experience.

Tuition and Financial Assistance

	Ohio Residents	Non-Ohio Residents
Below 11 Credit Hours	$536 per credit hour	$999 per credit hour
11-18 Credit Hours	$5,896	$10,989

$600/semester MSBA Program fee.

More Tuition & Fees information available here.

Scholarships:

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

Faculty

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

ROUZBEH RAZAVI, PH.D.

Director, MSBA Program

MURALI SHANKER, PH.D.

ALAN BRANDYBERRY, D.B.A.

CJ WU, PH.D.