Recommended prerequisite for participation in the module

Basic knowledge about Python is recommended, including running Python scripts and basic data structures. This can be obtained, e.g., in online material that will be made available to the students.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Data wrangling concepts and techniques, including data cleaning, data transformation, and data preparation for analyses.
• Data exploration to identify relevant patterns and preliminary insights.
• Data frames as data structures and the fundamental operations to manipulate them.
• Effective visualisation, including the principles for creating meaningful visualisations.

Skills

• Basic data manipulation to perform data wrangling in Python (e.g., pandas).
• Perform data cleaning, normalisation and scaling on data frames in Python.
• Construction of summary tables based on different stratifications.
• Creation of plots using Python’s plot libraries, such as MatPlotLib, Seaborn, or Plotly.
• Evaluate and improve plots to achieve clarity and precision.

Competences

• Analyse and explore datasets to understand their data and discover patterns.
• Design and implement basic data pipelines that include wrangling, analysis and visualisation.
• Solve simple problems through data wrangling and visualisation.

Type of instruction

Types of instruction are listed at the start of §17; Structure and contents of the programme.

Extent and expected workload

Expected module workload is 150 hours.

Exam

Exams

Name of exam	Programming for Data Wrangling and Visualisation
Type of exam	Oral exam
ECTS	5
Permitted aids	Please see the module description in Moodle.
Assessment	Passed/Not Passed
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures