M.S. in Business Analytics | College of Business Administration | Kent State University

M.S. in Business Analytics

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 be increasing your viability in a competitive market four sought-after analytics professionals.

Rouzbeh

Rouzbeh Razavi, Ph.D.

Director, MSBA Program
rrazavi@kent.edu
 


Program Features


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 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
  • 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

As of Fall 2018:

Domestic Students: $525 per credit hour*
International Students: $979 per credit hour*

*$600/semester fee for MSBA Program.

More Tuition & Fees information available here.


Graduate Assistantships and Scholarships:

Graduate assistantships and scholarships are available. These awards are very competitive and are awarded on the basis of academic merit. Graduate assistantships consist of a tuition waiver and stipend paid for work performed in the department.
 


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 


ASLI M. ARIKAN, PH.D.

Corporate Strategy, Corporate Finance, Growth of New Public Firms

ILGAZ ARIKAN, PH.D.

Competitive Strategy, International Business, Entrepreneurship

ALAN BRANDYBERRY, D.B.A.

Information Systems, Operations Research, Business Analytics, Management Science

PRATIM DATTA, PH.D.

Information Technology, Systems Security, Business Analytics, Infrastructure and Process Redesign and Engineering

NATALIA DRAGAN, PH.D.

Applied Mathematics, Statistics, Data Mining, Business Analytics

AL GUIFFRIADA, PH.D.

Operations, Supply Chain Management, Statistics, Management Science

MARY HOGUE, PH.D.

Industrial and Organizational Psychology, Differential Work Experiences of Men and Women

DEBORAH ERDOS KNAPP, PH.D.

HRM and OB, HR Analytics and ERP Systems

DONG-HEON (AUSTIN) KWAK, PH.D.

Online Donations, Charity Website Design, ERP, Business Intelligence

JULIA LEVASHINA, PH.D.

Human Resource Management, Impression Management, Biodata and Personality Measures

FELIX OFFODILE, PH.D.

Operations, Supply Chain and Technology Management, Statistics

EDDY PATUWO, PH.D.

Operations Research, Neural Networks, Statistics, Operations Management

GRETA L. POLITES, PH.D.

Habits/Resistance to Change, Business Intelligence Administration, Data Mining, Business Analytics

MURALI S. SHANKER, PH.D.

Simulation, Neural Networks, Optimization, Business Analytics

GEOFFREY STEINBERG, PH.D.

Database Management, Data Visualization, Web/Mobile Development and Programming

 


Contact Us
 

 Rouzbeh Razavi, Ph.D.
 Director, MSBA Program
 rrazavi@kent.edu
 330-672-2282

 

 

 

 

 

 

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