Data analysis in business Field of study: Computer Science
Programme code: W4-N2IN19.2021

Module name: Data analysis in business
Module code: W4-IN-N2-20-F-ADwB
Programme code: W4-N2IN19.2021
Semester:
  • winter semester 2023/2024
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: Polish
Form of verification: course work
ECTS credits: 4
Description:
Data analysis in business aims at developing skills of using statistical population characteristics and using data mining models for business data analysis. The goal of the course is also to improve knowledge of classic and modern data analysis techniques on the example of financial data. Topics: 1. Gathering, development and graphic presentation of data. 2. Elements of business data descriptive analysis. 3. Analysis of correlation, dependence and regression. 4. Application of classification and regression trees for business data analysis. 5. Application of technical and fundamental analysis to financial data. 6. Application of neural networks for business data analysis.
Prerequisites:
(no information given)
Key reading:
1. F. Provost, T. Fawcett, Data Science for Business. O'Reilly, 2013 2. D. Larose, Discovering Knowledge in Data. Wiley & Sons, 2005 3. D. Larose, Data Mining Methods and Models. Wiley & Sons, 2006 4. Murphy J., Technical Analysis of the Financial Markets, Study Guide for Technical Analysis of the Financial Markets, New York Institute of Finance, 1999
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student has knowledge about the average measures, the volatility measures and the asymmetry measures and uses them in order to perform a descriptive analysis of business data. The student has knowledge about the issues of interdependence analysis, correlation and regression analysis to discover relationships occurring in business data. [M_001]
 K_W01 [1/5] K_W09 [1/5]
The student has knowledge about classification and regression trees, neural networks, fundamental and technical analysis used to analyse business and financial data. [M_002]
 K_W09 [1/5]
She/He can make an initial assessment of business data, present it in an appropriate form, select the model or models suitable for analysis. She/He can compare the obtained results and draw conclusions based on them. [M_003]
 K_U01 [1/5] K_U08 [1/5] K_K04 [1/5]
She/He can use the available programs for business data analysis. [M_004]
 K_U09 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Examination reports [W_001]
Preparation of written reports and their oral presentation at a specified time as a verification of acquired skills during problems' solving.
 M_001 M_002 M_003 M_004
Written test [W_002]
Verification of knowledge and skills based on the analysis of tasks solutions during written test.
 M_001 M_002 M_003
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
Lecture presenting concepts and facts from the scope of program contents which are listed in the module and illustrating them with numerous examples
 15
Self-study of lectures and literature
 15 Written test [W_002]
laboratory classes [Z_002]
Laboratory, during which students perform exercises with the help of the teacher, which develop the skills listed in the set of learning outcomes of the module
 30
Self-improvement of skills listed in the set of learning outcomes of the module
 60 Examination reports [W_001] Written test [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)