Content, progress and pedagogy of the module

M2 aims to give students insight into network and unstructured data types such as natural language and text, as well as state-of-the-art approaches to map and analyse these data. Insights and techniques gained in this module will allow students to approach real-world problems in marketing (Who are the main influencers among our customers?), management (Can we identify new discourses in the communication within our organisation?), business economics (Can language patterns be used to understand R&D intensity across companies?), political science (How is a political candidate perceived by a certain demographic, based on their social network statements?), and sociology (How is a person’s behaviour and characteristics affected by their social network?).

Upon completion, students will have built a solid knowledge foundation within network theory and analysis, computational linguistics and broader (unstructured) data processing. The module is application-focused, and thus students will gain a variety of skills to utilise relational and unstructured text data for analysis purposes.

Learning objectives

Knowledge

The objective is that the student after the module possesses the necessary knowledge on:

• the conceptual particularities and explanatory power of relational and network data as well as unstructured and text data including understanding of the epistemology of relational and language data.
• the interplay between network-theory concepts and real-world networks as well as the representation and analysis of real-world  phenomena from unstructured data and text.
• the integration of relational and unstructured, particularly language data into statistical and machine learning based methodologies as well as the interpretation of findings in business, economics and social science contexts.

Skills

The objective is that the student after the module possesses the necessary skills in:

• sourcing, storing and pre-processing network and text data – including using various techniques of vectorisation -,  calculating and interpreting essential statistic metrics, and integrating network and text indicators into machine learning pipelines.
• visualising text data, networks and interaction pattern.
• performing tasks such as grammar-based labelling and modifications on text data, automated summarisation, sentiment analysis, and extracting entities from text.

Competences

The objective is that the student after the module possesses the necessary competences in:

• representing any real-life complex systems as networks.
• identifying latent patterns, structures and interactions of entities in these systems, and exploring the interplay between the structure of systems and their performance as well as particular features and behaviour of individual entities.
• utilising natural language data for various types of mapping and analysis.

Type of instruction

For information see § 17.

Exam

Prerequisite for enrollment for the exam

• A prerequisite for participating in the exam is that the student has participated actively in developing written material during the module.

Exams