2025/2026
Data Mining
Type:
Optional course (faculty)
Delivered by:
Department of Statistics and Data Analysis
Where:
Faculty of Economic Sciences
When:
1 module
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
28
Course Syllabus
Abstract
Data Mining is the creation of new knowledge to solve business problems, by using business knowledge to discover and interpret patterns in data. The course contents both methodology of the data mining process and practical guide to data preparation, modeling, and deployment analytics into business processes. It covers the following main types of modelling techniques: classification, regression, clustering, anomaly detection and association rules. Special attention will be given to the hands-on data analysis using available software tools.
Learning Objectives
- To introduce students to the concept of data mining process aimed at solving business problems. To provide knowledge of the basic data mining techniques. To gain practical skills in in data analysis, building models and evaluating their quality.
Expected Learning Outcomes
- • Knowledge of methodology of the data mining process
- • Understanding and practical skills in data preparation.
- • Understanding and practical skills in modeling: classification, regression, clustering, association rules and anomaly detection
- • Understanding and practical skills in evaluation of modeling results.
Course Contents
- 1. Data Mining process
- 2. Data preparation
- 3. Classification
- 4. Regression
- 5. Clustering techniques
- 6. Outlier detection.
- 7. Association rules
- Литература по курсу "Современные методы обработки статистических данных (англ)"
Assessment Elements
- Контрольная работа
- Домашняя работа
- Контрольная работа
- Домашняя работа
- Домашняя работа
- Домашняя работа
- Активность на занятиях
Interim Assessment
- 2025/2026 1st module0.25 * Активность на занятиях + 0.25 * Домашняя работа + 0 * Домашняя работа + 0 * Домашняя работа + 0.25 * Домашняя работа + 0.25 * Контрольная работа
Bibliography
Recommended Core Bibliography
- Data Mining : учеб. пособие, Чубукова, И. А., 2008
- Han, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques (Vol. 3rd ed). Burlington, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=377411
- Han, J., Kamber, M., Pei, J. Data Mining: Concepts and Techniques, Third Edition. – Morgan Kaufmann Publishers, 2011. – 740 pp.
- Robert Nisbet, John Elder, & Gary D. Miner. (2009). Handbook of Statistical Analysis and Data Mining Applications. Academic Press.
- The elements of statistical learning : data mining, inference, and prediction, Hastie, T., 2017
- Toomey, D. (2014). R for Data Science. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=933765
- Understanding intercultural communication, Ting-Toomey, S., 2012
- Международная статистика : учебник для вузов / под редакцией Б. И. Башкатова, А. Е. Суринова. — 3-е изд., перераб. и доп. — Москва : Издательство Юрайт, 2025. — 593 с. — (Высшее образование). — ISBN 978-5-534-10635-0. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/559700 (дата обращения: 04.07.2025).
- Суринов, А. Е. Экономическая статистика в страховании : учебник для вузов / А. Е. Суринов. — 2-е изд., перераб. и доп. — Москва : Издательство Юрайт, 2025. — 276 с. — (Высшее образование). — ISBN 978-5-534-05414-9. — Текст : электронный // Образовательная платформа Юрайт [сайт]. — URL: https://urait.ru/bcode/561080 (дата обращения: 04.07.2025).
Recommended Additional Bibliography
- Data mining with R : learning with case studies, Torgo, L., 2017