Course objectives: Introduce students to methods for retrieving data from existing sources. Cleaning and normalizing raw data. Connecting data from different sources. Preparing data for graphical display. Selecting
and applying an appropriate data visualization technique.
Enrolment requirements and/or entry competences required for the course: None
Learning outcomes at the level of the programme to which the course contributes: Recognize, use, integrate, and document data from various sources
Expected learning outcomes at the level of the course:
(1) Retrieve data from various sources
(2) Create a new database by combining data from various sources
(3) Visualizing data
Course content (syllabus):
(1) Introduction
(2) Introduction to Structured Query Language (SQL)
(3) Introduction to Application programming interfaces (API)
(4) Introduction to SPARQL Protocol and RDF Query Language (SPARQL)
(5) Regular expressions
(6) Web scraping – HTML and CSS basics
(7) Web scraping – extracting data from static web pages
(8) Web scraping – extracting data from dynamic web pages
(9) Data wrangling – cleaning and normalizing raw data
(10) Data wrangling – combining data from different sources
(11) Data wrangling – preparing data for plotting
(12) Data visualization – display of qualitative data
(13) Data visualization – display of quantitative data
(14) Data visualization – geographical data
(15) Concluding remarks
Format of instruction: lectures, seminars and workshops, exercises
Student responsibilities: The course consists of a combination of lectures, seminars, and exercises in which the contents of the lectures are practically mastered. Within the seminar, a practical seminar
assignment consisting of retrieving and connecting data from various sources. Student progress in the course is evaluated through individual work within exercises, seminar assignments and through a final written exam.
Monitoring student work: Class attendance, practical work, written exam
