Data mining Field of study: Computer Science
Programme code: W4-N2IN19.2022

Module name: Data mining
Module code: W4-IN-N2-20-2-ED
Programme code: W4-N2IN19.2022
Semester:
  • summer semester 2024/2025
  • winter semester 2024/2025
  • summer semester 2023/2024
  • winter semester 2023/2024
  • summer semester 2022/2023
Language of instruction: Polish
Form of verification: exam
ECTS credits: 4
Description:
The goal is to introduce the listener to data mining methods, classification issues, grouping and induction of rules from data. content: 1. Preliminary concepts 2. Preparation and initial data processing 3. Clustering 4. Basics of classification 5. Decision rules 6. Association rules 7. Decision trees 8. Classifier teams 9. Linear regression
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]
Is aware of the impact of data mining methods and data types on the quality of knowledge explored. [M_001]
 K_K02 [1/5]
Has knowledge of data types, similarity measures, classification quality measures [M_002]
 K_W09 [2/5]
Has knowledge of data preprocessing (discretization, normalization, empty data) [M_003]
 K_W09 [2/5]
Has knowledge of choosing the right method of exploration depending on the type of input data and expected results [M_004]
 K_W09 [3/5]
Is able to prepare a set for analysis (discretize data, normalize data, empty data) [M_005]
 K_U03 [2/5] K_U08 [2/5] K_U09 [4/5]
Is able to implement selected cluster analysis algorithms [M_006]
 K_U08 [2/5] K_U09 [4/5]
Has basic knowledge of association and decision-making rules and approaches to constructing them [M_007]
 K_U09 [2/5]
Has basic knowledge of the subject and is able to determine the function of linear regression. [M_008]
 K_W01 [1/5] K_W09 [1/5]
He can classify data and properly interpret the result [M_009]
 K_W09 [3/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
exam (test) [W_001]
Knowledge verification based on the content presented in the lecture. The exam consists of both open and closed theory questions.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007 M_008 M_009
Projects and reports [W_002]
Developing projects with reports for them within a specified period as a verification of the skills acquired in solving problems.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007 M_008 M_009
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]
Transferring the content of education in verbal form using audiovisual means and other written teaching aids.
 20
Preparation for the exam.
 20 exam (test) [W_001]
laboratory classes [Z_002]
Detailed preparation of students to solve tasks with an indication of the methodology of the procedure, an indication of the order of performed activities.
 20
Preparation for the laboratory. Student's independent solution of tasks assigned to the laboratory, preparation of reports
 60 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)