Computational Mathematics
Course: Geophysics and computer processing of geological and geophysical data
Structural unit: Educational and Scientific Institute "Institute of Geology"
Title
Computational Mathematics
Code
ОК 30
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2022/2023
Semester/trimester when the component is delivered
5 Semester
Number of ECTS credits allocated
3
Learning outcomes
- Know the basic methods and tools for all sections of data analysis;
- Be able to use basic methods and tools from all sections of data analysis;
- Demonstrate skills of interaction with other people, ability to work in teams;
- Be able to organize their own activities and get results within a limited time. Demonstrate the ability to self-study and continue professional development;
- Combine methods of mathematical and computer modeling with informal expert analysis procedures to find optimal solutions.
Form of study
Full-time form
Prerequisites and co-requisites
Know: probability theory and mathematical statistics.
Be able to: apply knowledge of probability theory and mathematical statistics.
Have basic skills: solve problems in probability theory and mathematical statistics.
Course content
The discipline has the following sections: data processing, correlation analysis, regression analysis, analysis of variance, covariance analysis, time series analysis, classification problems. The main task is to provide students with basic knowledge in all major sections of data analysis and gain experience in working with relevant software in solving applied problems. Uses concepts from probability theory and mathematical statistics, mathematical analysis and algebra.
Recommended or required reading and other learning resources/tools
1. Slabospitsky O.S. Data analysis. Front trim: front. pos_b. / O. S. Slabospitsky. - K.: All-Russian Orthodox Church "Kiev University", 2001.
2. Slabospitsky O.S. Fundamentals of correlation analysis of data: beginning. pos_b. /ABOUT. S. Slabospitsky. — K.: All-Russian Orthodox Church "Kiev University", 2006.
3. Slabospitsky O.S. Variance analysis of data: navch. pos_b. / O. S. Slabospitsky. – K.: All-Russian Orthodox Church "Kyiv University", 2013.
4. Draper N., Smith G. Applied regression analysis, 2007.
Planned learning activities and teaching methods
Lectures, practical tasks in the form of essays and independent work of students
Assessment methods and criteria
The control is carried out according to the modular rating system and includes: practical work (where students must demonstrate the quality of acquired knowledge and solve problems using the methods and tools outlined by the teacher) and defense of abstracts, and written tests. The final assessment is conducted in the form of a written test.
Language of instruction
Ukrainian
Lecturers
This discipline is taught by the following teachers
Departments
The following departments are involved in teaching the above discipline