Module also offered within study programmes:
General information:
Name:
Statistical modeling and data analysis in scientific research
Course of study:
2019/2020
Code:
ZSDA-3-0150-s
Faculty of:
Szkoła Doktorska AGH
Study level:
Third-cycle studies
Specialty:
-
Field of study:
Szkoła Doktorska AGH
Semester:
0
Profile of education:
Academic (A)
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
prof. nadzw. dr hab. inż. Saramak Daniel (dsaramak@agh.edu.pl)
Dyscypliny:
inżynieria chemiczna, inżynieria lądowa i transport, inżynieria materiałowa, inżynieria środowiska, górnictwo i energetyka
Module summary

Student nabędzie wiedzę i umiejętności w zakresie wykorzystanie technik i programów komputerowych do analizy danych przemysłowych oraz modelowanie operacji technologicznych w górnictwie i przeróbce surowców mineralnych. Student nabędzie umiejętności tworzenia raportów produkcyjnych związanych z oceną efektywnością przebiegu procesu przemysłowego oraz eksploracji danych.

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 PhD Student is aware of the need of statistical description and modeling of mining and mineral processing, aiming at better understanding of analysed problems SDA3A_K01, SDA3A_K03 Activity during classes
Skills: he can
M_U001 PhD Student is able to run statistical analysis of the problem on the basis of empirical process data SDA3A_U03, SDA3A_U01 Execution of laboratory classes,
Execution of a project,
Activity during classes
Knowledge: he knows and understands
M_W001 PhD Students gain knowledge related to process data analysis SDA3A_W03, SDA3A_W01 Activity during classes
M_W002 PhD Students know techniques of mining and mineral processing modeling SDA3A_W03 Activity during classes
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
60 30 0 15 15 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 PhD Student is aware of the need of statistical description and modeling of mining and mineral processing, aiming at better understanding of analysed problems + - + - - - - - - - -
Skills
M_U001 PhD Student is able to run statistical analysis of the problem on the basis of empirical process data - - + + - - - - - - -
Knowledge
M_W001 PhD Students gain knowledge related to process data analysis + - - - - - - - - - -
M_W002 PhD Students know techniques of mining and mineral processing modeling + - + + - - - - - - -
Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 103 h
Module ECTS credits 4 ECTS
Udział w zajęciach dydaktycznych/praktyka 60 h
Preparation for classes 20 h
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 10 h
Realization of independently performed tasks 10 h
Examination or Final test 2 h
Contact hours 1 h
Module content
Lectures (30h):

- Descriptive statistics used in analysis of process data.
- Data mining
- Statistical models of process run
- Contemporary models and trends in mineral prrocessing and mining modelling
- economic and technological assessment of models

Laboratory classes (15h):

During laboratory classes PhD Students undergo practical analyse, application and development of issues and topics given on lectures.

Project classes (15h):

Practical projects based on individual research area of PhD Students and issues presented during lectures

Additional information
Teaching methods and techniques:
  • Lectures: Nie określono
  • Laboratory classes: Nie określono
  • Project classes: Nie określono
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Final mark from project: on the basis of prepared statistical analysis of given problem. In case of unsuccesful score it is possible to provide another analysis or to improve the one assessed as negative.
Final mark from lectures: on the basis of PhD Student activity. In case of problems with obtaining a positive score, PhD student is obliged to pass a short test related to issues presented during lectures.

Participation rules in classes:
  • Lectures:
    – Attendance is mandatory: No
    – Participation rules in classes: Nie określono
  • Laboratory classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
  • Project classes:
    – Attendance is mandatory: Yes
    – Participation rules in classes: Nie określono
Method of calculating the final grade:

Final mark: arithmetic value of mark from project, laboratory classes and lectures.

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

Self-study of missed topic(s)

Prerequisites and additional requirements:

Basic knowledge of statistics

Recommended literature and teaching resources:

1. Sobczyk: Statystyka, PWN Warszawa, 2005
2. Krysicki i in.: Rachunek prawdopodobieństwa i statystyka matematyczna w zadaniach, cz.2
3. Greń: Statystyka matematyczna, modele i zadania. PWN, 1974

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

1. Tumiadajski T., Saramak D.: Metody i modele statystyki matematycznej w przeróbce mineralnych, Wydawnictwo AGH, 2009.
2. Foszcz D., Saramak D., Tumidajski T., Niedoba T., Gawenda T.: Możliwości poprawy dokładności aproksymacji krzywych składu materiałów uziarnionych, Possibilities of adjusting the adequacy of grained materials particle size distribution approximation, Górnictwo i Geoinżynieria Akademia Górniczo-Hutnicza im. Stanisława Staszica, Kraków; ISSN 1732-6702. Tyt. poprz.: Górnictwo (Kraków). 2010 R. 34 z. 4/1 s. 37–47
3. Tumidajski T., Foszcz D., Jamróz D., Niedoba T., Saramak D.: Niestandardowe metody statystyczne i obliczeniowe w opisie procesów przeróbki surowców mineralnych, (Non classical statistical and calculation methods in mineral processing description). Instytut Gospodarki Surowcami Mineralnymi i Energią Polskiej Akademii Nauk., Kraków: Wydawnictwo IGSMiE PAN, 2009. 125 s.. ISBN 978-83-60195-62-

Additional information:

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