Data Mining

Course: Economics (English/Ukrainian Taught)

Structural unit: Faculty of Economics

Title
Data Mining
Code
ВБ
Module type
Вибіркова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2021/2022
Semester/trimester when the component is delivered
6 Semester
Number of ECTS credits allocated
6
Learning outcomes
PLO 5. To apply analytical and methodical tools for substantiating proposals and making managerial decisions by various economic agents (individuals, households, enterprises and government authorities). PLO 8. To apply appropriate economic and mathematical methods and models for solving economic problems. PLO 10. To analyze the functioning and development of economic entities, to identify functional areas, to calculate the relevant indicators that characterize the performance of their activities. PLO 13. To identify sources and understand the methodology of determination and methods for obtaining socioeconomic data, collect and analyze the necessary information, calculate economic and social indicators.
Form of study
Full-time form
Prerequisites and co-requisites
For studying the course students should acquire knowledge on “Mathematics”, “Probability Theory and Mathematical Statistics”, “Econometrics” and “Mathematical Optimization”; skills of analysis, synthesis, processing and visualization of economic information, identification of sources and methods of obtaining socio-economic data, collecting the necessary information, implementation of economic and mathematical methods, models and information technologies to solve economic problems.
Course content
The curriculum consists of the following content modules: Module 1. “Classic technologies of Data Mining” is devoted to the study of technologies for collecting and preparing data for analysis, solving clustering and association problems using appropriate software tools. Module 2. “Modern models of intelligent computing” covers the models of neural networks, decision trees, hybrids and some other approaches to solve Data Mining problems.
Recommended or required reading and other learning resources/tools
1. Tan P., Steinbach M., Kumar V., Karpatne A. Introduction to Data Mining. 2nd edition, Pearson Education, 2019, 864 p. 2. Witten Ian H., Frank Eibe, Hall Mark A. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2016, 654 p. 3. Zaki Mohammed J., Meira JR. Wagner. Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, 2014, 607 p. 4. Han J. Data Mining: Concepts and Techniques 3rd edition Morgan Kaufmann, 2011, ‎ 800 p. 5. Rangaswamy A. Data Science Methodology (Big Data Intelligence Book 2). Amazon Digital Services LLC. 2016 6. Salcedo J., McCormick K. IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions. Packt Publishing; 2017, 238 р.
Planned learning activities and teaching methods
lectures, lab practicals, individual student’s self-study
Assessment methods and criteria
Semester evaluation: 1. Oral examination – 5 points / 3 points; 2. Solving problems– 20 points / 12 points; 3. Essay – 5 points / 3 points 4. Module test (2 MT, 10 points max each) – 20 points / 12 points; 5. Individual project (1 project, 25 points) – 25 points / 15 points; 6. Final test– 25 points / 15 points Final evaluation in the form of the test with a maximum score of 25 points (marginal score of 15 points).
Language of instruction
English

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline