Data Analysis in Business Field of study: Computer Science
Programme code: 08-S2INIA15.2016

Module name: Data Analysis in Business
Module code: 08-IN-ISI-S2-ADwB
Programme code: 08-S2INIA15.2016
Semester: summer semester 2017/2018
Language of instruction: English
Form of verification: course work
ECTS credits: 3
Description:
Analysis of business data aims at developing skills of using statistical population characteristics and constructing and using data mining for data analysis. The goal of the subject is also perfecting the knowledge of classic and modern techniques of data analysis on the example of financial data. The following topics are planned to realize: 1. Gathering, development and graphic presentation of data. 2. Elements of business data descriptive analysis. 3. Analysis of phenomena interdependence, correlation and regression. 4. Use of technical and fundamental analyses for financial data analysis. 5. Use of issues connected with Fibonacci and Pivot levels. 6. Use of neural networks for business data analysis. Aim of the classes is educating students’ skills of using the most important methods used in data mining.
Prerequisites:
(no information given)
Key reading:
(no information given)
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]
Can acquire knowledge from literature, data bases and other appropriately chosen sources, can integrate acquired knowledge, interpret and also draw conclusions and formulate and justify opinions. [ADwB -U_3]
 K_2_A_I_U01 [2/5]
Is able to provide mathematical description of the chosen technical indicator for data analysis. [ADwB -U_4]
 K_2_A_I_U07 [1/5]
Can take advantage of available programs to perform data mining. [ADwB -U_5]
 K_2_A_I_U18 [4/5] K_2_A_I_U22 [1/5]
Student has knowledge of average measure, measure of variability and measure of asymmetry to perform descriptive analysis of business data. The student uses issues of interdependence analysis and correlation and regression analyses to study dependencies occurring in business data. [ADwB -W_1]
 K_2_A_I_W03 [2/5]
Student possesses knowledge about preliminary development of data and using k-closest neighbor classifier, naïve Bayes classifier, classifier of classification trees and regression, neural networks, affinity analysis and sequence for the use of data analysis. [ADwB -W_2]
 K_2_A_I_W17 [4/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Constant evaluation [ADwB _w_1]
Verifying according to answers to the asked questions concerning lectured topics and knowledge of homeworks solutions
 ADwB -U_3 ADwB -U_4 ADwB -U_5 ADwB -W_1 ADwB -W_2
Written tests [ADwB _w_2]
Verification of skills on the basis of solved tasks analysis during written tests with use of computer.
 ADwB -U_3 ADwB -U_4 ADwB -U_5 ADwB -W_1 ADwB -W_2
Written elaboration [ADwB _w_3]
Skills verification through written elaboration of the material connected with performing data set analysis and interpretation of obtained results
 ADwB -U_3 ADwB -U_4 ADwB -U_5 ADwB -W_1 ADwB -W_2
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 [ADwB _fs_1]
Lecture presenting notions and facts from the range of program content mentioned in module description and illustrating the content with numerous examples
 15
Self – study of lectures and additional literature indicated in syllabus
 15 Constant evaluation [ADwB _w_1] Written tests [ADwB _w_2] Written elaboration [ADwB _w_3]
laboratory classes [ADwB _fs_2]
Laboratory class, during which the students with help of the teacher, make exercises forming skills enumerated in the set of module educational effects
 30
Self-improvement of skills enumerated in the set of module educational effects
 30 Constant evaluation [ADwB _w_1] Written tests [ADwB _w_2] Written elaboration [ADwB _w_3]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)