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M.S. in Business Analytics (In-Person)

Unlock the power of data and advance your career with the M.S. degree in Business Analytics. Our program provides students with the knowledge and skills needed to leverage data analytics to make informed business decisions. 

With coursework designed to build proficiency in data analysis, data mining and data visualization, graduates are well-prepared to meet the demands of today's data-driven business environment. With our certificate, you'll be prepared to pursue a career in data analytics, business intelligence or consulting.

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In-Person

Combine your business skills and analytical acumen to become a professional with a Master of Science degree in Business Analytics (MSBA).

Admission into the MSBA Program is available in the Fall and Spring - no GRE/GMAT required!

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. By earning a master’s in business analytics, you will increase your viability in a competitive market for sought-after analytics professionals.

 

What Is a Master’s in Business Analytics Program?

An MSBA degree prepares students to enter the field of business analytics. The structure of the program provides students with the definitive knowledge they need to obtain information from a given data set and ultimately make informed business decisions.

 

What Are the Benefits of Completing the MSBA In-Person?

While our online MSBA program provides a number of ways for instructors and students to communicate, the in-person program provides the opportunity to interact frequently with the instructor and fellow students in real time. In-person learning provides opportunities for socialization and network building. A classroom environment can help students focus on learning and presents the ability to receive immediate feedback. Finally, an in-person experience allows you to enjoy Kent State's vibrant atmosphere and take classes in a brand new building - Crawford Hall.

Three-Foci Stem Program

The language of business today is dependent on information and data management. The Â鶹´«Ã½ 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.

A Multi-Disciplinary Graduate Program

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.

What Are the Core Competencies for the Business Analytics Degree?

By earning your M.S. in Business Analytics,  you will increase your viability in a competitive market as 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 in 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

 

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
Programming and Software Tools

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 
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

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

 

  • 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 

Program Information for M.S. in Business Analytics (In-Person)

Program Description

Full Description

The Master of Science degree in Business Analytics provides students with a comprehensive knowledge of analytics that balances the technologies, analytical methods and business expertise needed to glean useful information from data to make strategic business decisions. The language of business today is dependent on information and data management, and 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.

Students in the Business Analytics major gain the technical, analytical, communication, decision-making and leadership skills needed to be successful business analysts. The curriculum includes integrative capstone analysis projects, and there is an internship option for additional professional development through the on-site Career Services Office.

Admissions for M.S. in Business Analytics (In-Person)

For more information about graduate admissions, visit the graduate admission website. For more information on international admissions, visit the international admission website.

Admission Requirements

  • Bachelor's degree from an accredited college or university1
  • Minimum 3.000 undergraduate GPA on a 4.000-point scale
  • Official transcript(s)
  • Résumé
  • Goal statement
  • Two letters of recommendation
  • English language proficiency - all international students must provide proof of English language proficiency (unless they meet specific exceptions to waive) by earning one of the following:2
    • Minimum 79 TOEFL iBT score
    • Minimum 6.5 IELTS score
    • Minimum 58 PTE score
    • Minimum 110 DET score
1

Students entering the program are expected to have the requisite backgrounds in statistics, mathematics, computers and business required for the program. At a minimum, students should have general knowledge of inferential statistics, adequate general business knowledge, basic knowledge of business information systems and technologies and a solid understanding of algebra and general mathematics with some exposure to calculus. Students may fulfill deficiencies in these prerequisites by taking BA 24056CIS 24053, MATH 11012, MGMT 24163 or equivalents as applicable. The business analytics program director may consider concurrent enrollment according to the strength of the student’s baccalaureate curriculum and preparedness and the courses proposed to be taken concurrently with the prerequisites.

2

International applicants who do not meet the above test scores may be considered for conditional admission.

Application Deadlines

  • Fall Semester
    • Application deadline: March 15 (international student) and June 15 (domestic student)
  • Spring Semester
    • Application deadline: October 1 (international student) and December 1 (domestic student)

Applications submitted after these deadlines will be considered on a space-available basis.

Learning Outcomes

Program Learning Outcomes

Graduates of the program will be able to:

  1. Identify key characteristics of the business problem.
  2. Identify opportunities and constraints of various data analytical frameworks and tools.
  3. Formulate appropriate data analytic techniques to solve the business problem.
  4. Perform necessary data preparation steps (retrieve, clean and manipulate data).
  5. Demonstrate necessary theoretical knowledge and practical skills to implement several data-analytic frameworks using different tools.
  6. Lead and work with teams to frame the business problem.
  7. Convey in writing the outcomes of analytics for stakeholders.
  8. Use visual outcomes of analytics to communicate orally effective messages for stakeholders.
Coursework

Program Requirements

Major Requirements

Major Requirements
BA 54038ANALYTICS IN PRACTICE 3
BA 64018QUANTITATIVE MANAGEMENT MODELING 3
BA 64036BUSINESS ANALYTICS 3
BA 64037ADVANCED DATA MINING AND PREDICTIVE ANALYTICS 3
BA 64060FUNDAMENTALS OF MACHINE LEARNING 3
BA 64061ADVANCED MACHINE LEARNING 3
BA 64082DATABASE MANAGEMENT AND DATABASE ANALYTICS 3
Major Electives, choose from the following:6
BA 54011
SYSTEMS SIMULATION
BA 54050
DATA VISUALIZATION
BA 54052
TEXT ANALYSIS AND NATURAL LANGUAGE PROCESSING
BA 64028
GLOBAL SUPPLY CHAIN BUSINESS MODELS
BA 64092
INTERNSHIP IN BUSINESS ANALYTICS
BA 64099
CAPSTONE PROJECT IN BUSINESS ANALYTICS
CS 63015
DATA MINING TECHNIQUES
CS 63016
BIG DATA ANALYTICS
ECON 62054
ECONOMETRICS I
EMAT 61010
ENTERPRISE ARCHITECTURE
EMAT 64210
DATA SCIENCE
HI 60411
CLINICAL ANALYTICS
KM 60370
SEMANTIC ANALYSIS METHODS AND TECHNOLOGIES
MGMT 64160
LEADERSHIP AND ORGANIZATIONAL CHANGE
MKTG 65057
MARKETING RESEARCH
Culminating Requirement
BA 64092INTERNSHIP IN BUSINESS ANALYTICS 3
or BA 64099 CAPSTONE PROJECT IN BUSINESS ANALYTICS
Minimum Total Credit Hours:30

Graduation Requirements 

Minimum Major GPA Minimum Overall GPA
- 3.000
  • No more than one-half of a graduate student’s coursework may be taken in 50000-level courses.
  • Grades below C are not counted toward completion of requirements for the degree.

Minimum 30 credit hours required for the degree is the assumption that students do not have any unmet requirements they need to be successful in the program. The business analytics program coordinator may make further determination of a student’s preparedness for the program and what prerequisite courses, if any, may be required.

Program Delivery
  • Delivery:
    • Fully online
    • In person
  • Location:
    • Kent Campus
Accreditation for M.S. in Business Analytics (In-Person)

AACSB International - The Association to Advance Collegiate Schools of Business

Examples of Possible Careers and Salaries for M.S. in Business Analytics (In-Person)

Chief executives

-10.0%

decline

287,900

number of jobs

$185,950

potential earnings

Data scientists and mathematical science occupations, all other

30.9%

much faster than the average

33,200

number of jobs

$98,230

potential earnings

General and operations managers

5.8%

faster than the average

2,486,400

number of jobs

$103,650

potential earnings

Management analysts

10.7%

much faster than the average

876,300

number of jobs

$87,660

potential earnings

Operations research analysts

24.8%

much faster than the average

105,100

number of jobs

$86,200

potential earnings

Statisticians

34.6%

much faster than the average

42,700

number of jobs

$92,270

potential earnings

Notice: Career Information Source
* Source of occupation titles and labor data comes from the U.S. Bureau of Labor Statistics' . Data comprises projected percent change in employment over the next 10 years; nation-wide employment numbers; and the yearly median wage at which half of the workers in the occupation earned more than that amount and half earned less.

Commonly Asked Questions About Our In-Person Business Analytics Degree

If you have further questions about Kent State’s MSBA program, our team has prepared answers below for some of the most frequent queries we receive.

What Careers Can I Pursue with an MSBA Degree?

Some of the careers that you could pursue with your M.S. in Business Analytics degree include the following:

  • Chief executive
  • Data scientist
  • Management analyst
  • Statistician
  • Market research analyst
  • Business intelligence analyst
Can I Transfer Credits from Another Graduate Program to the MSBA Program?

Yes, typically graduate credits can be transferred from a previous university or a different Kent State graduate program. When transferring your credit, it is important to note that a maximum of 12 hours can be transferred to your MSBA.

For more information regarding our transfer credit requirements, please visit the .

Is a Master’s in Business Analytics Online Valued by Employers?

Yes! Completing your M.S. in Business Analytics  will give you an advantage in the job market. Candidates with master’s degrees from accredited universities are sought after by employers, particularly those in the business analytics sector.

With that in mind, our faculty at Â鶹´«Ã½ encourage potential students to consider the program that is best suited to their schedule and timeframe, either the in-person or online option. No matter which format you choose, our dedicated faculty are ready to equip you with the tools you need to succeed in the business analytics industry.

What Is the Application Process Like for Kent State’s M.S. in Business Analytics Online?

Admission to the MSBA program occurs during the fall and spring semesters and is highly competitive. We strongly recommend applying to our in-person business analytics degree early, as applications are reviewed on a rolling basis throughout the admission cycle.

What Should I Consider When Choosing an MSBA Degree?

When choosing where you want to complete your MSBA program, it is important to consider the program’s curriculum, faculty expertise, industry connections, reputation, alumni network, career placement opportunities, and more. Additionally, it is vital that the program aligns with your personal and career goals. Kent State is the perfect destination because we have expert faculty who can give you the skills you need to prepare for life after graduation, as well as financial aid opportunities to help you cover costs. 

Boost Your Career with Our M.S. in Business Analytics Online Program

Ready to gain the skills and experience you need to advance in your field? Contact the team at Kent State today by either applying to the program or requesting more information.

We look forward to assisting you!

 

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