Bachelor
2021/2022
Data Mining
Type:
Elective course (Economics and Statistics)
Area of studies:
Economics
Delivered by:
Department of Statistics and Data Analysis
Where:
Faculty of Economic Sciences
When:
4 year, 1 module
Mode of studies:
offline
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
30
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
- Mastering clustering methods
- Mastering data preparation
- Mastering Regression.....
- Mastering the classification
- Mastering the detection of outliers
- Mastering the process of data mining
- Mastering the rules of the association
Course Contents
- 1. Data Mining process
- 2. Data preparation
- 3. Classification
- 4. Regression
- 5. Clustering techniques
- 6. Outlier detection. Feature selection and dimensionality reduction
- 7. Association rules
Assessment Elements
- Homework 1 (Themes 1-3)
- Homework 2 (Themes 4-6)
- Activity in the classroom
- Final test
- Finail.test
Interim Assessment
- 2021/2022 1st module0.25 * Activity in the classroom + 0.25 * Homework 1 (Themes 1-3) + 0.25 * Finail.test + 0.25 * Homework 2 (Themes 4-6)
Bibliography
Recommended Core Bibliography
- 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.
Recommended Additional Bibliography
- 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