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Payments can be sent to ÐÔ¸£ÎåÔÂÌì University, Attn:  Bursar's Office, P.O. Box 5190, Kent, OH 44242-0001.  Check payments should be made payable to ÐÔ¸£ÎåÔÂÌì University.  Please be sure to provide identifying information including the student’s name and ÐÔ¸£ÎåÔÂÌì 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:
Harbourt Hall, Attn: Bursar’s Office, Room 130, 615 Loop Road, Kent, Ohio 44242

In-person payments can be made on the Kent Campus using the payment drop box located outside of Harbourt Hall (615 Loop Rd)

Please note that the Financial, Billing and Enrollment Center and Regional Campuses are unable to accept in-person payments.

Please be sure to provide identifying information including the student’s name and ÐÔ¸£ÎåÔÂÌì University ID with the payment.

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.

You can pay via the Online Payment Portal 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 Card payments, KSU accepts American Express, Discover, MasterCard, and Visa.

• 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 

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

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 

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 

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 

Principles of Machine Learning and Data Modeling

• Data Structures and Types of Variables
• Supervised vs.