Recommended prerequisite for participation in the module

The module builds on knowledge acquired in the modules “Programming for Data Wrangling and Visualisation”, "Introduction to Statistics", and "Introduction to AI Techniques".

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Database and SQL, including key concepts of relational databases, entity-relationship (ER) modelling and create-read-update-delete (CRUD) operations.
• Techniques to access and load data from external sources, including API, CSV, Excel and JSON, and the Extract Transform Load (ETL) process.
• Big data principles, including volume, variety and velocity.
• Methods for visualising high-dimensional data like, e.g., PCA, t-SNE, and/or UMAP.
• Techniques to create interactive visualisations to explore data and patterns in plots, e.g., faceted browsing, linked views and interactive dashboards.

Skills

• Retrieve and manipulate data with SQL: interpret and compose SQL statements to extract, filter and aggregate data stored in relational databases.
• Usage of libraries in Python to retrieve, transform and integrate data from external data sources.
• Design and implement interactive visualisations in Python using dedicated libraries, such as Plotly, Bokeh and Dash.
• Design and develop interactive dashboards using dedicated tools like Tableau or Dash.

Competences

• Advanced data wrangling techniques to manage large data volumes.
• Create and manipulate data frames using data stored in databases.
• Design and implement complex data visualisation solutions to address stakeholder requirements.
• Solve complex problems using advanced data wrangling and data visualisation solutions.

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	Advanced Data Wrangling and Interactive Visualisation
Type of exam	Oral exam
ECTS	5
Permitted aids	Please see the module description in Moodle.
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures