Content, progress and pedagogy of the module

The analysis of larger datasets is a productive way to obtain new empirical insights into real-world phenomena. With a critical attitude to the creation and interpretation of data, this course provides a basic step-by-step hands-on introduction of the processes of data gathering, data cleaning, explorative data analysis and visualization. As an example of an object-oriented language useful for this purpose, we use Python with its large array of functional modules (libraries) and integrations for our exercises. The process of selection, cleaning and analysis of data lead us to discuss reliability, predictability, categorization and information security. This module is anchored in the Research group of Communication, Media and Information technologies, Department of Electronic Systems.  

Learning objectives

Knowledge

• explain gathering of / obtaining larger datasets and assessing the quality of the data, i.e. its discursive power.
• describe assessment of the reliability, predictive power and generalizability of the processed data and visualizations of it. 
• account for advanced data gathering and data processing techniques. 

Skills

• cleaning and preparing larger datasets for analysis. 
• writing simple code snippets in a scripting language, e.g. Python. 
• handling data in a scripting tool. 
• structured debugging and problem-solving during the scripting process. 
• visualizing data via relevant types of data diagrams. 

Competences

• analyzing datasets both with inductive / explorative approaches and driven by a hypothesis. 
• assessing the quality of both data, findings and data visualizations. 
• presenting transparent descriptions of applied data mining processes.  
• assessing the coherence of the data processing in relation to the result presented. 
• ability to reflect about the relation between data, findings and discourses. 

Type of instruction

Course module. Please refer to §17 of the curriculum about the structure and content of the programme.

Exam

Exams