Infopaket

Basics of Data Mining

ДВС.2.06

Вибіркова дисципліна для ОП

First

2023/2024

8 Semester

3

PLO12. Apply methods and algorithms of computational intelligence and smart data mining in the tasks of classification, forecasting, cluster analysis, and finding associative rules using multidimensional data analysis software support tools based on DataMining, TextMining, WebMining technologies. PLO18.2 . Analyse, evaluate, and select instrumental and computing tools, paradigms, technologies, algorithmic and software solutions in the design and development of software systems.

Distance form

Know: mathematic methods, programming principles, algorithm development principles, software, data structures, and knowledge development principles. To be able to: implement in practice instrumental means of software design and development; formulate and research mathematical models, in particular discrete mathematical models, justify the choice of methods and approaches used to solve theoretical and applied problems in the field of computer science. Have basic skills: programming, design, and development of software.

The aim of the discipline is to acquire fundamental knowledge in the theoretical aspects of Data Mining technology, respective methods, and possibilities of their application. The educational discipline "Fundamentals of Data Mining" is a component of the program for training specialists at the first (bachelor's) level of higher education in the field of knowledge 12 "Information Technologies" from the specialty 122 "Computer Science", within the educational and professional program - "Informatics". This discipline is an optional course in the "Informatics" program. Taught in the 8th semester of the 4th course with a total of 90 hours. (3 ECTS credits) including lectures – 28 hours, consultations – 2 hours, independent work – 60 hours. The course includes 2 parts and 2 tests. The discipline ends with a credit in the 8th semester.

1. Leskovec J., Rajaraman A., Ullman J. D. Mining of massive datasets. – Cambridge University Press, 2014. 2. Dawn Griffiths, Head First Statistics: A Brain-Friendly Guide Taschenbuch – 2008. 3. Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists – 2016. 4. Tan P. N., Steinbach M., Kumar V. Introduction to data mining. 1st. – 2005. 5. Han J., Pei J., Kamber M. Data mining: concepts and techniques. – Elsevier, 2011. 6. Zaki M. J., Meira Jr W., Meira W. Data mining and analysis: fundamental concepts and algorithms. – Cambridge University Press, 2014. 7. Aggarwal C. C. Data mining: the textbook. – Springer, 2015.

Lectures, independent work.

Semester Assessment: 1. Tests: LO 1.1., LO 1.2, LO 1.3, LO 2.1, LO 2.2 — 53 points/32 points. 2. Independent work: LO 2.1, LO 2.2, LO 4.1 –– 47 points/28 points. Final assessment in the form of a credit. Credit points are defined as the sum of grades/points for all successfully assessed learning outcomes provided by this program.

Ukrainian