Module also offered within study programmes:
General information:
Name:
Data mining
Course of study:
2015/2016
Code:
BIT-1-706-s
Faculty of:
Geology, Geophysics and Environmental Protection
Study level:
First-cycle studies
Specialty:
-
Field of study:
Applied Computer Science
Semester:
7
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
prof. dr hab. inż. Walanus Adam (a@adamwalanus.pl)
Academic teachers:
dr inż. Chuchro Monika (chuchro@geol.agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 Potrafi zbadać jakość danych IT1A_K04, IT1A_K01 Activity during classes
Skills
M_U001 Potrafi wykonać regresję w MS Excel, Matlab i Statistica IT1A_U01, IT1A_U16, IT1A_U14, IT1A_U02 Activity during classes
Knowledge
M_W001 Zna podstawy statystyki IT1A_W01 Activity during classes
M_W002 Potrafi obliczyć i zinterpretować statystyki jednej zmiennej IT1A_W01 Activity during classes
M_W003 Potrafi zinterpretować macierz korelacji IT1A_U01, IT1A_U07, IT1A_U06, IT1A_U02 Activity during classes
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Others
E-learning
Social competence
M_K001 Potrafi zbadać jakość danych - - - - - - + - - - -
Skills
M_U001 Potrafi wykonać regresję w MS Excel, Matlab i Statistica + - - - - - + - - - -
Knowledge
M_W001 Zna podstawy statystyki + - - - - - + - - - -
M_W002 Potrafi obliczyć i zinterpretować statystyki jednej zmiennej + - - - - - + - - - -
M_W003 Potrafi zinterpretować macierz korelacji + - - - - - + - - - -
Module content
Lectures:

1. Variables, experimental data, measurements, scales, dependent vs. independent variables
2. Relations between variables
3. Statistical significance of results (p-value)
4. Normal distribution and other probability distributions
5. Basic statistics
6. Regression
7. Classification
8. Multivariate analysis
9. Data quality, data clearing, transformations
10. Multidimensional scaling
11. Machine Learning
12. Data mining in industrial engineering

Practical classes:

1. Variables, experimental data, measurements, scales, dependent vs. independent variables
2. Relations between variables
3. Statistical significance of results (p-value)
4. Normal distribution and other probability distributions
5. Basic statistics
6. Regression
7. Classification
8. Multivariate analysis
9. Data quality, data clearing, transformations
10. Multidimensional scaling
11. Machine Learning
12. Data mining in industrial engineering

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 90 h
Module ECTS credits 3 ECTS
Participation in lectures 15 h
Realization of independently performed tasks 15 h
Participation in practical classes 30 h
Preparation for classes 30 h
Additional information
Method of calculating the final grade:

Średnia z ocen zdobywanych w trakcie ćwiczeń

Prerequisites and additional requirements:

Znajomość metod numerycznych i elementów programowania

Recommended literature and teaching resources:

WWW, MS Excel, Statistica

Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

udział „praktycznych” punktów ECTS: 2
udział „teoretycznych” punktów ECTS: 1