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Natural Language Processing 

The purpose of this course is to introduce students to the theory and practice of applying natural language processing (NLP) in economics and business. We cover all steps in the data science pipeline of transforming textual data into numbers that are relevant for decision making. We assume no prior knowledge concerning specific NLP related subjects and start off with a general introduction to text mining.


After the introduction, we cover the most used unsupervised, such as matrix factorization and topic modelling, and (semi-)supervised, such as document classification, and text mining methods. In addition, the course is designed to enable students to study the principles of constructing linear econometric time series models and how these models can be used in various practical contexts.

This module can be taken as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

By the end of this course, participants should have knowledge and ability to: 

Relevant natural language programming techniques and their application to a wide range of empirical issues.

Integration of natural language programming techniques in econometric prediction models. 

Use of the open source statistical software Python for solving NLP problems in economics and business.

Present, interpret and analyse information in numerical form.

Desired Skills

By the end of this course, students should be able to: 

Engage in abstract thinking by extracting the essential features of large textual corpora to solve an economic and managerial problem. 

Communicate and present complex arguments in oral and written form with clarity and succinctness.

Present, interpret and analyse information in numerical form.

Utilise effectively statistical and other packages. 



Natural Language Processing is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.


The total number of contact hours is 15 hours. This leaves 85 hours for private study.



This module consists of 2- hour lectures per day for 5 days, plus a 1 - hour tutorial per day. 

During their private study, students should read the course literature, work on practical exercises, and write a project report for the examination.

The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact

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