Course Name
Mathematical Methods of Experimental Data Processing
Course Overview 
Level of study	Master Degree	
Workload	ECTS: 5
Total Hours: 144
Contact Hours: 72
•	Lectures: 36
•	Labs: 36
•	Seminars: 0	
Course Code		
Semester	Winter, Summer	
Prerequisites	Computer Science, Calculus	
Course Objectives	Getting skills and knowledge in the field of data processing: creating mathematical models and decision-making based on experimental data	
Learning Outcomes
	Getting skills and knowledge in the field of data processing: creating mathematical models and decision-making based on experimental data
By the end of the course the student will know: 
Basic statistical tool for data analysis;
The students will be able to:
Make decision and provide evidence based on experimental data
The students will have the experience in:
Performing statistical analysis using special software packages	
Syllabus
	Introduction to statistic
Descriptive statistic
Student’s t-test
Correlation analysis
Regression analysis
Analysis of variance
Design of experiment	
Labs
	Descriptive statistic
Student’s t-test
Correlation analysis
Regression analysis
Analysis of variance
Design of experiment	
Projects		
Assessment	Exam	
Resources	Resource type: Background textbook
Larson, Ron, Elizabeth Farber, and Elizabeth Farber. Elementary statistics: Picturing the world. No. QA276. 12. L373 2009. Pearson Prentice Hall, 2009.

Mann, Prem S. Introductory statistics. John Wiley & Sons, 2007.

Siegel, Andrew. Practical business statistics. Academic Press, 2011.
	
Instructors	Ilya Ogol, http://portal.tpu.ru/SHARED/i/ILYAO	
Примечание: пример описания дисциплины можно посмотреть по ссылке: http://www.ecs.soton.ac.uk/module/COMP6233#overview