By Mail

Payments can be sent to Kent State University, Attn:  Bursar's Office, P.O. Box 5190, Kent, OH 44242-0001.  Check payments should be made payable to Kent State University.  Please be sure to provide identifying information including the student’s name and Kent State University ID with the payment.

If you are sending your payment overnight utilizing UPS or Fed EX mail services, please send to the Bursar’s Office street address below:
Schwartz Center, Attn: Bursar’s Office, Room 131, 800 E. Summit St., Kent, Ohio 44242

In-Person

In-person payments can be made using the payment drop box located outside the Bursar’s Office (131 Schwartz) or at Kent State Regional Campuses.

For Kent Campus, please note that the One Stop for Student Services, located on the first floor of the University Library, is unable to accept payments.

Please be sure to provide identifying information including the student’s name and Kent State University ID with the payment.

Monthly Payment Plan

Education expenses can be easier to manage when spread over predictable monthly payments. Our monthly payment plan, administered by Transact, is an alternative to one large payment and may help limit loan borrowing. The plan is available during fall and spring semesters only. The enrollment fee is $55 per semester. For more information and to enroll in the plan, please visit the payment plan website.

Online Payments

You can pay via the Online Payment Center using eChecks, Credit Card, or International payments.  There is no fee for payments made using eChecks.  You will need to enter your U.S. checking or savings account information and payment will be made electronically.  For Credit Card payments, KSU accepts American Express, Discover, MasterCard, and Visa payments.

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 

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 

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 
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