Scripting languages in data analysis Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Scripting languages in data analysis
Module code: W4-INA-S2-20-F-JSwAD
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 module aims at introducing the students with advanced data analysis possibilities 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
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 algorithms. [M_001]
 K_W02 [1/5] K_W09 [1/5]
The student knows how to analyse data, is familiar with the algorithms used in data analysis, and knows how to interpret the results. [M_002]
 K_W04 [1/5] K_W09 [1/5]
The student can select and implement the algorithm for data analysis. [M_003]
 K_U08 [1/5] K_U09 [1/5] K_U10 [1/5]
The student can interpret the result of data analysis and present the results of data analysis motivate the techniques used. [M_004]
 K_U03 [2/5] K_U04 [2/5] K_U10 [1/5]
The student can develop a scheme of data handling, aimed at their correct analysis. [M_005]
 K_U01 [1/5] K_U02 [1/5] K_U03 [1/5]
The student can implement an automated data analysis system, working individually or in a team. [M_006]
 K_U02 [1/5] K_U09 [2/5] K_U10 [3/5]
The student is aware of the impact of algorithms on the results of data analysis. [M_007]
 K_K01 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Reports [W_001]
The students prepare written reports within a specified period as verification of skills gained during problem-solving.
 M_001 M_002 M_003 M_004 M_007
Project [W_002]
The students develop an individual or group project with documentation of the data analysis system.
 M_001 M_002 M_003 M_005 M_006 M_007
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 multimedia tools and discuss issues related to the analysis and automation of data analysis in scripting languages.
 15
The lectures prepare the students to perform laboratory exercises. They are the practical presentation of issues discussed during the lectures.
 20 Project [W_002]
laboratory classes [Z_002]
The classes prepare the students to perform laboratory exercises. They are the practical presentation of issues discussed during the lectures.
 30
The students prepare for the laboratory classes and passing the lecture test. The students prepare for completing laboratory tasks and 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)