Data visualization Field of study: Computer Science
Programme code: W4-N2IN19.2021

Module name: Data visualization
Module code: W4-IN-N2-20-F-WD
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:
The aim of the module is to introduce students with the possibilities of advanced data visualization 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]
Has knowledge about the use and implementation of data visualization methods. [M_001]
 K_W02 [1/5] K_W04 [1/5] K_W09 [3/5]
Has knowledge of how to process and visualize data, about the methods used and how to interpret the results. [M_002]
 K_W04 [1/5] K_W09 [2/5]
Is able to select and implement the appropriate method of data visualization [M_003]
 K_U01 [1/5] K_U03 [1/5]
Is able to interpret the result of data visualization and justify the techniques used [M_004]
 K_U08 [1/5] K_U09 [1/5] K_U10 [1/5]
Can implement an automated data visualization system, working individually or in a team. [M_005]
 K_U02 [1/5] K_U03 [1/5] K_U09 [1/5]
Is aware of the process of improvement and tracking the latest solutions in the field of data visualization [M_006]
 K_K01 [2/5] K_K03 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Reports [W_001]
Preparation of written reports, their completion within a specified period as a verification of skills acquired during problem solving.
 M_001 M_002 M_003 M_004 M_006
project [W_002]
Develop individual or group project and documentation system data visualization.
 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]
Lectures conducted using multimedia tools, discussing issues related to the visualization and automation of data visualization in scripting languages.
 15
Preparation for laboratories and passing the lecture.
 20 project [W_002]
laboratory classes [Z_002]
Preparing students to perform lab exercises. Practical presentation of issues discussed during lectures.
 30
Preparation for laboratory exercises. Self-solving laboratory exercises. Preparation of 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)