(in Polish) Laboratorium ekspertyz cyfrowych (II)- ćwiczenia WSE-BD-LEC-(II)-ćw

https://teams.microsoft.com/l/channel/19%3A5FDGKN2erK7rKld3oidb-dS1v-I2i57iMyF1ovFokao1%40thread.tacv2/?groupId=d7b5ce58-fb4a-4c57-ba21-478b1035a35c

Introduction to GAMS (8h):
Basics of GAMS syntax
Structure of an optimization model
Defining variables and constraints
First models and their implementation
Interpreting results
Linear programming - simplex method (10h):
Linear programming theory
Formulating decision problems
Implementing the simplex method in Excel
Sensitivity analysis
Case study
Transportation problems (6h):
Modeling transportation problems
Methods of solving transportation problems
Implementation in Excel
Optimization of transportation costs
Analysis of real cases
Integer programming (2h):
Introduction to integer programming
Location problems
Assignment problems
Implementation in GAMS and Excel
Practical applications
Final project (4h):
Implementation of a practical project
Presentation of results
Discussion and feedback

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

sociology

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

Student effort: Student activity Student workload in hours participation in laboratories 30 preparation for intergroup discussions 15 consultation 10 time to write intergroup work 15 preparation for exam 20 TOTAL NUMBER OF HOURS 90 NUMBER OF ECTS POINTS 88 hours / 25 (30) hours ≈ 4 1ECTS corresponds to 25—30h of student workload, according to the study program and own work

Subject level

intermediate

Learning outcome code/codes

BDAS2_W02, BDAS2_W06, BDAS2_W11 BDAS2_U01, BDAS2_U02, BDAS2_U05, BDAS2_U06, BDAS2_U07 BDAS2_K01, BDAS2_K02, BDAS2_K03, BDAS2_K04, BDAS2_K05, BDAS2_K06enter learning outcome code/codes

Type of subject

obligatory

Preliminary Requirements

Big Data Engineering and Analysis, Basic Statistics, Computer Programming

Course coordinators

Second Bwanakare

Learning outcomes

Knowledge (W)
Knows and understands the methodology of conducting digital expertise in social research
Has knowledge of analytical tools used in the analysis of digital data
Understands the ethical aspects of working with digital data in the context of sociological research

Skills (U)
Can design and conduct digital expertise using appropriate methods and tools
Can analyze and interpret digital data in the context of social phenomena
Can create reports and present the results of digital expertise

Social competences (K):
Is ready to critically evaluate his/her own knowledge and skills in the field of conducting digital expertise
Shows ethical responsibility when working with digital data
Can work in a research team implementing projects in the field of digital expertise

Assessment criteria

The final grade consists of the group project and its presentation, plus an oral exam.

The final grade includes the grade for the project and oral exam (80%) and the grade for activity during classes (20%).

10 points - grade: 5.0

8-9 points - grade: 4.5

7-8 points - grade: 4.0

6-7 points - grade: 3.5

5-6 points - grade: 3.0

below 5 points - grade: 2.0

2 - insufficient - The student did not complete the work or the work is not his/her own achievement, it is chaotic in relation to various concepts of digital expertise methods. The student does not understand the basic concepts related to the topic and avoids discussions on linear optimization

3 - sufficient - The student defines and describes problems in the

range of digital expertise. He/she would be ready to expand his/her knowledge and skills for professional purposes.

4 - good - The student did a good job and correctly identified problems and concepts. Is able to select and apply appropriate techniques depending on the type of problem. The student initiates discussions related to the presented methods of digital expertise

5 -very good- The student did a good job and correctly identified problems and concepts. Is able to select and apply appropriate methods depending on the type of problem. Is able to correctly interpret the solution and refer to the literature proposed in the syllabus. The student initiates discussions related to the issues of digital expertise, is ready to apply analytical skills in using the research methodology in the field of digital expertise.

Bibliography

Mandatory literature:
1) McCarl B., "GAMS User Guide"
2) Zbigniew Jędrzejczyk, Karol Kukuła, Jerzy Skrzypek, Badania operacyjne w przykładach i zadaniach, • Wydanie/Copyright: wyd. 7, 2015, wydawnictwo Naukowe PWN, 2022,
3) Tadeusz Trzaskalik, zadania z badań operacyjnych, częśc i wyd.2 poprawione, Uniwersytet Ekonomiczny W Katowicach, 2022, ISBN: 978-83-7875-784-9
4) Sikora W. (red.): Badania operacyjne, Polskie Wydawnictwo Ekonomiczne, Warszawa 2008.

Additional literature:
1) Winston W.L., "Microsoft Excel: Data Analysis and Business Modeling
2).Gajda J., Kucharski A.: Badania operacyjne. Metody i zastosowania, Wydawnictwo Uniwersytetu Łódzkiego, Łódź 2008.
3) Lipiec-Zajchowska M.: Wspomaganie procesów decyzyjnych. Tom III. Badania operacyjne, Wydawnictwo C.H. Beck, Warszawa 2003
4) Teacher's own materials.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WSE-BD-LEC-(II)-ćw in USOSweb