Module also offered within study programmes:
General information:
Name:
Data mining
Course of study:
2015/2016
Code:
BOS-1-511-s
Faculty of:
Geology, Geophysics and Environmental Protection
Study level:
First-cycle studies
Specialty:
-
Field of study:
Environmental Protection
Semester:
5
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Responsible teacher:
dr inż. Chuchro Monika (chuchro@geol.agh.edu.pl)
Academic teachers:
prof. dr hab. inż. Walanus Adam (a@adamwalanus.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)
Skills
M_U001 Potrafi obliczyć i zinterpretować statystyki jednej zmiennej OS1A_U05, OS1A_U15 Execution of laboratory classes
M_U002 Potrafi zinterpretować macierz korelacji OS1A_U05, OS1A_U15 Execution of laboratory classes
M_U003 Potrafi wykonać regresję w MS Excel, Matlab i Statistica OS1A_U05, OS1A_U15 Execution of laboratory classes
M_U004 Potrafi zbadać jakość danych OS1A_U05, OS1A_U15, OS1A_U14 Execution of laboratory classes
Knowledge
M_W001 Zna podstawy statystyki OS1A_W07 Execution of laboratory 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
Skills
M_U001 Potrafi obliczyć i zinterpretować statystyki jednej zmiennej + - - + - - - - - - -
M_U002 Potrafi zinterpretować macierz korelacji + - - + - - - - - - -
M_U003 Potrafi wykonać regresję w MS Excel, Matlab i Statistica + - - + - - - - - - -
M_U004 Potrafi zbadać jakość danych + - - + - - - - - - -
Knowledge
M_W001 Zna podstawy statystyki + - - + - - - - - - -
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

Project classes:

1. Real measurements with a tape measure, organizing spreadsheet
2. Modelling random variables (Excel)
3. Searching for extreme values, estimating low probabilities
4. Modelling normal distribution
5. Calculation of basic statistics
6. Calculation of Regression
7. Performing classification (Statistica)
8. Performing multivariate analysis (Statistica)
9. Transforming data, Box-Cox
10. Statistica – Data miner I
11. Statistica – Data miner II, machine Learning
12. Industial statistics, SPC, QCC

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 87 h
Module ECTS credits 3 ECTS
Participation in lectures 28 h
Participation in project classes 14 h
Realization of independently performed tasks 30 h
Preparation for classes 15 h
Additional information
Method of calculating the final grade:

Średnia z ocen zdobywanych w trakcie ćwiczeń laboratoryjnych

Prerequisites and additional requirements:

Znajomość metod numerycznych i elementów programowania

Recommended literature and teaching resources:

WWW, Pomoc MS Excel, Pomoc i Podręcznik Statystyki Statistica
http://www.statsoft.pl/textbook/stathome.html

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

Additional scientific publications not specified

Additional information:

None