Data mining Field of study: Computer Science
Programme code: W4-S2INA19.2020

Module name: Data mining
Module code: W4-INA-S2-20-2-ED
Programme code: W4-S2INA19.2020
Semester: winter semester 2021/2022
Language of instruction: English
Form of verification: exam
ECTS credits: 4
Description:
The module's goal is to introduce the listener to data mining methods, classification issues, grouping and induction of rules from data. The module comprises: 1. Preliminary concepts 2. Data preprocessing 3. Clustering 4. Basics of classification 5. Feature selection 6. Decision rules 7. Testing statistical hypotheses 8. Association rules 9. Decision trees 10. Classifiers 11. Linear regression 12. Neural networks
Prerequisites:
(no information given)
Key reading:
Larose Daniel „Metody i modele eksploracji danych”. PWN, 2008 Hand David, Manila Heikki, SmythPadhraic, „Principles of data mining”, MIT Press, 2001. YanchangZhao, „R and data mining”, Example and CaseStudies, Elsevier, 2013. Morzy Tadeusz, „Eksploracja danych”, PWN, 2013 Koronacki Jan, Mielniczuk Jan „Statystyczne systemy uczące”, WNT, 2005 Ćwik Jan, Koronacki Jacek, Statystyczne systemy uczące się w oparciu o pakiet R”, OWPD, 2009 Gatnar Eugeniusz, Walesiak Marek, „Statystyczna analiza danych z wykorzystaniem programu R”, PWN, 2009 Han J., Kamber M.: Data Mining: Concepts and Techniques, Morgan Kaufmann 2011 E. Gatnar, Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji, PWN, 2008
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student is aware of the impact of data mining methods and data types on the quality of knowledge explored. [M_001]
 K_K02 [1/5]
The student knows data types, similarity measures, and classification quality measures. [M_002]
 K_W09 [2/5]
The student knows data preprocessing (discretisation, normalisation, empty data). [M_003]
 K_W09 [2/5]
The student remembers the rules of choosing the right method of exploration depending on the type of input data and expected results. [M_004]
 K_W09 [3/5]
The student can prepare a set for analysis (discretise data, normalise data, fill in empty data). [M_005]
 K_U03 [2/5] K_U08 [2/5] K_U09 [4/5]
The student can implement selected cluster analysis algorithms. [M_006]
 K_U08 [2/5] K_U09 [4/5]
The student can determine the quality of classification [M_007]
 K_U08 [2/5] K_U09 [3/5]
The student has a basic knowledge of association and decision-making rules and approaches to constructing them. [M_008]
 K_U09 [2/5]
The student can present selected algorithms for constructing decision and association rules and their application. [M_009]
 K_W02 [2/5] K_W09 [2/5]
The student has a basic knowledge of feature selection. [M_010]
 K_W09 [1/5]
The student can classify data and interpret the result correctly. [M_011]
 K_W09 [3/5]
The student has a basic knowledge of decision trees and teams of classifiers. [M_012]
 K_W05 [1/5] K_W09 [1/5]
The student can present selected approaches to the construction of decision trees and teams of classifiers. [M_013]
 K_U08 [1/5]
The student has a basic knowledge of the subject and can determine the function of linear regression. [M_014]
 K_W01 [1/5] K_W09 [1/5] K_U08 [1/5]
The student has a basic knowledge of neural networks. [M_015]
 K_W09 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Exam (Test) [W_001]
The exam verifies the knowledge based on the content presented during the lectures. It comprises both open and closed-ended theoretical questions.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007 M_008 M_009 M_010 M_011 M_012 M_013 M_014 M_015
Projects and reports [W_002]
The students develop projects with reports for them within a specified period to verify their skills in solving problems.
 M_001 M_002 M_005 M_006 M_007 M_008 M_009 M_010 M_011 M_012 M_013 M_014 M_015
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
The lectures have a verbal form with the use of audiovisual means and other written teaching aids.
 30
The lectures prepare for the exam.
 15 Exam (Test) [W_001]
laboratory classes [Z_002]
The laboratory classes prepare the students to solve tasks by emphasising the proceeding method and the sequence of operations.
 30
The student self-studies for the laboratory classes, completes assigned tasks, and prepares the reports.
 45 Projects and reports [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)