Data analysis in business Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Data analysis in business
Module code: W4-INA-S2-20-F-ADwB
Programme code: W4-S2INA19.2021
Semester:
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: English
Form of verification: course work
ECTS credits: 4
Description:
Data analysis in business aims at developing statistical population characteristics and use data mining models for business data analysis. The course's goal is also to improve knowledge of classic and modern data analysis techniques on the example of financial data. The list of the topics comprises: 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 of 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 knows the average measures, the volatility measures and the asymmetry measures and uses them to perform a descriptive analysis of business data. The student knows the issues of interdependence analysis, correlation and regression analysis to discover business data relationships. [M_001]
 K_W01 [1/5] K_W09 [1/5]
The student has knowledge about classification and regression trees, neural networks, the fundamental and technical analysis used to analyse business and financial data. [M_002]
 K_W09 [1/5]
They make an initial assessment of business data, present it appropriately, and select the model or models suitable for analysis. They 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]
They can use the selected program 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]
The students prepare written reports and present them orally at a specified time. The presentations are to verify the skills acquired during the problems' solving stage.
 M_001 M_002 M_003 M_004
Test [W_002]
The students write the test designed to verify their knowledge and skills in solving specific tasks.
 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]
The lectures present the concepts and facts from the curriculum listed in the module and illustrate them with many examples.
 15
The student must self-study the content from the lectures and the literature listed in the course syllabus.
 15 Test [W_002]
laboratory classes [Z_002]
The students perform exercises with the teacher's help during the laboratory classes, which develops the skills listed in the module's set of learning outcomes.
 30
The students self-improve the skills listed in the module's set of learning outcomes.
 60 Examination reports [W_001] 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)