Module also offered within study programmes:
General information:
Name:
Principles of Data and Process Mining
Course of study:
2019/2020
Code:
GIPZ-2-402-CP-n
Faculty of:
Mining and Geoengineering
Study level:
Second-cycle studies
Specialty:
Controlling of production processes
Field of study:
-
Semester:
4
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Part-time studies
Course homepage:
 
Responsible teacher:
prof. nadzw. dr hab. inż. Brzychczy Edyta (brzych3@agh.edu.pl)
Module summary

Main issues covered by lectures are related to data mining and process mining techniques as well as tools used in advanced analytics.

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: is able to
M_K001 Student can perform data or process analysis in project group. IPZ2A_K01 Activity during classes
Skills: he can
M_U001 Student can perform data mining analysis IPZ2A_U02, IPZ2A_U01, IPZ2A_U04 Completion of laboratory classes
M_U002 Student can perform process mining analysis. IPZ2A_U02, IPZ2A_U01, IPZ2A_U04 Completion of laboratory classes
Knowledge: he knows and understands
M_W001 Student knows principles of knowledge discovery process in enterprise. IPZ2A_W02 Test
M_W002 Student knows selected methods of Data Mining and Machine Learning. IPZ2A_W02 Completion of laboratory classes,
Test
M_W003 Student knows selected issues related to process mining methods. IPZ2A_W02 Completion of laboratory classes,
Test
Number of hours for each form of classes:
Sum (hours)
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
15 9 0 6 0 0 0 0 0 0 0 0
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
Prace kontr. przejść.
Lektorat
Social competence
M_K001 Student can perform data or process analysis in project group. - - + - - - - - - - -
Skills
M_U001 Student can perform data mining analysis - - + - - - - - - - -
M_U002 Student can perform process mining analysis. - - + - - - - - - - -
Knowledge
M_W001 Student knows principles of knowledge discovery process in enterprise. + - - - - - - - - - -
M_W002 Student knows selected methods of Data Mining and Machine Learning. + - - - - - - - - - -
M_W003 Student knows selected issues related to process mining methods. + - - - - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 78 h
Module ECTS credits 3 ECTS
Udział w zajęciach dydaktycznych/praktyka 15 h
Preparation for classes 30 h
Realization of independently performed tasks 30 h
Examination or Final test 2 h
Contact hours 1 h
Module content
Lectures (9h):

1. Knowledge Discovery Process in Databases
2. Exploratory tasks in Data Mining
3. Selected method of Data Mining: cluster analysis, decision trees, association rules
4. Introduction to Process Mining
5. Process discovery methods
6. Conformance checking and enhancement of the process
7. Mining of additional process perspectives

Laboratory classes (6h):

1. Introduction to STATISTICA software and exploratory data analysis
2. Cluster analysis, decision trees, association rules
3. Introduction to ProM software
4. Process discovery from event logs
5. Conformance checking and enhancement of the process
6. Additional process perspectives analysis

Additional information
Teaching methods and techniques:
  • Lectures: The content presented at the lecture is provided in the form of a multimedia presentation in combination with a classical lecture panel enriched with demonstrations relating to the presented issues .
  • Laboratory classes: During the laboratory classes, students independently solve the practical problem, choosing the right tools. The leader stimulates the group to reflect on the problem, so that the obtained results have a high substantive value.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

The final grade is based on the test and the grade from laboratory classes.
2 attempts for each element are given.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Students participate in the classes learning the next teaching content according to the syllabus of the subject. Students should constantly ask questions and explain doubts. Audiovisual recording of the lecture requires the teacher's consent.
  • Laboratory classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Students carry out laboratory exercises in accordance with materials provided by the teacher.
Method of calculating the final grade:

The final grade is based on the test and the laboratory classes (both positive grades) and is calculated according to the algorithm: OK = 0.5 * T+ 0.5 * L
where: T– test grade, L – laboratory grade

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

Laboratory classes need to be completed with other group.
Additional presentation can be required on given topic.

Prerequisites and additional requirements:

Basic statistical background is needed.

Recommended literature and teaching resources:

Larose T.D., 2006: Odkrywanie wiedzy z danych. Wyd. Naukowe PWN, Warszawa
Hand D., Mannila H., .Smyth P., 2002: Principles of Data Mining, MIT press
Van der Aalst W., 2016: Process Mining Data Science in Action, 2nd edition, Springer

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

Brzychczy E., 2007: Budowa modeli ekonometrycznych wybranych parametrów techniczno-ekonomicznych kopalni węgla kamiennego. Wiadomości Górnicze, 11
Brzychczy E., 2008: Analiza wykorzystania kombajnów chodnikowych w przodkach korytarzowych w kopalniach węgla kamiennego z zastosowaniem drzew decyzyjnych. Zarządzanie: doświadczenia i problemy. Red. W. Sitko. Wyd. System-Graf, Lublin
Brzychczy E., Stefaniak R., Maroszek Z., Siodłak Ł., 2010: Wykorzystanie wybranych technik Data Mining do analizy kompleksów ścianowych w KWK “Ziemowit”. Miesięcznik WUG Bezpieczeństwo i Ochrona Pracy w Górnictwie, nr 1
Brzychczy E., 2009: Techniki eksploracji danych w zagadnieniach eksploatacji górniczej złóż węgla kamiennego. Kwartalnik AGH, Górnictwo i Geoinżynieria, nr 3
Brzychczy E., 2009: Analiza wyposażenia przodków ścianowych na podstawie reguł asocjacyjnych. Wiadomości Górnicze, R.60, nr 3
Trzcionkowska A., Brzychczy E., 2016: Wykorzystanie reguł asocjacyjnych do analizy pracy wybranego urządzenia w oddziale wydobywczym. Inżynieria Mineralna, R. 17, nr 2

Additional information:

Written test – 5 questions.
Laboratory grade is based on realisation of individual data or process mining task.