Infopaket

Data Mining

ВБ

Вибіркова дисципліна для ОП

First

2023/2024

6 Semester

6

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.

Full-time form

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.

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.

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.

lectures, lab practicals, individual student’s self-study

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).

English