Data visualization Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Data visualization
Module code: W4-INA-S2-20-F-WD
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:
The aim of the module is to introduce students with the possibilities of advanced data visualisation with elements of automation using scripting languages such as Python or R.
Prerequisites:
(no information given)
Key reading:
1. John M. Quick, Statistical Analysis with R, Packt Publishing 2. Michael Dawson, Python Programming for the Absolute Beginner, 3. Wes McKinney, Python for Data Analysis, O'relly 4. Norman Matloff, The Art of R Programming: A Tour of Statistical Software Design 5. Mario Dobler, Data Visualization with Python, Packt Publishing 6. Thomas Rahlf, Data visualization with R, Springer
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 use and implementation of data visualisation methods. [M_001]
 K_W02 [1/5] K_W04 [1/5] K_W09 [3/5]
The student knows how and with which methods to process and visualise data and can interpret the results. [M_002]
 K_W04 [1/5] K_W09 [2/5]
The student can select and implement the method of data visualisation. [M_003]
 K_U01 [1/5] K_U03 [1/5]
The student can interpret the result of data visualisation and justify the techniques used. [M_004]
 K_U08 [1/5] K_U09 [1/5] K_U10 [1/5]
The student can implement an automated data visualisation system, working individually or in a team. [M_005]
 K_U02 [1/5] K_U03 [1/5] K_U09 [1/5]
The student is aware of the process of improvement and tracking the latest solutions in data visualisation. [M_006]
 K_K01 [1/5] K_K03 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Reports [W_001]
The student prepares written reports within deadlines as verification of skills gained during problem-solving
 M_001 M_002 M_003 M_004 M_006
Project [W_002]
The students develop individual or group projects and document system data visualisation.
 M_001 M_002 M_003 M_004 M_005 M_006
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 are conducted with the use of multimedia tools and discuss the issues related to the data visualisation and its automation in scripting languages.
 15
The lectures prepare the student for laboratory classes and passing the exam.
 20 Project [W_002]
laboratory classes [Z_002]
The classes prepare students to perform lab exercises and are a practical presentation of issues discussed during lectures.
 30
The student prepares for the laboratory exercises, solves laboratory exercises and prepares the final project.
 55 Reports [W_001] Project [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)