Overview
The curriculum is unique in its emphasis on data-heavy approaches to language and text from a Humanities perspective. Starting from scratch, the modules of the Master programme of Digital Text Analysis at the University of Antwerp cover a wide array of text technologies, ranging:
- from artificial intelligence (machine learning)
- over data science (statistics)
- to natural language processing (computational linguistics)
This ambitious programme seeks to prepare the next generation of highly self-reliant, culture-aware experts in text analytics, who are highly employable in a variety of research contexts across academia and the industry, including the cultural sector.
Starting from scratch, the curriculum covers a wide array of text technologies.
- Natural Language Processing: Technologies from computational linguistics are nowadays crucial in digital text analysis. This module offers an extensive introduction to modern pipelines in Natural Language Processing. Jointly, we work towards practical applications on textual data, as well as solutions for the many challenges that remain open in the field.
- Data Science: Employers in academia and the industry increasingly expect data scientists to be able to rapidly extract relevant insights from large document collections. In this module, we focus on exploratory data analysis and practical data visualization, involving challenging, real-world datasets.
- Machine Learning: Deep learning features prominently in this programme, as a crucial component of contemporary artificial intelligence. Modules focusing on neural networks are supplemented by a wide-ranging and in-depth survey of established, alternative methods from machine learning.
Bootcamp
A unique feature of this curriculum is the compulsory programming bootcamp at the beginning of the first semester. This 3-week course makes no assumptions whatsoever about the students’ coding proficiency and will provide a no-nonsense introduction to modern computer programming and the wider scientific ecosystem in computing.
Hands-on
Above all, this Master delivers a practice-oriented educational programme, for example through a project-based Master’s thesis and the possibility of an external internship with one of our many stakeholders in academia or the industry. On top of these, students can choose from a diverse array of elective modules from the departments of literature and linguistics.
Programme Structure
Courses include:
- Text as Data
- Corpus Studies
- Humanities Data Analysis
- Machine Learning
- Information Science
- Natural Language Processing
- Computational Literary Studies
Key information
Duration
- Full-time
- 12 months
Start dates & application deadlines
- Starting
- Apply before , International
- Apply before , EEA/EU
-
Language
Credits
Delivered
Disciplines
Data Science & Big Data Digital Communication Machine Learning View 3 other Masters in Machine Learning in BelgiumAcademic requirements
We are not aware of any academic requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
The Master is open to everyone holding one of the following degrees:
- Bachelor of History
- Bachelor of Philosophy
- Bachelor of Linguistics and Literary Studies
- Bachelor of Applied Language Studies
- Bachelor of Laws
- Bachelor of Communication Studies
- Bachelor of Sociology
- Bachelor of Political Science
- Bachelor of Social and Economic Sciences
- Other academic Bachelor degrees need permission from the faculty.
No previous experience with computing technology or programming is required (except from very basic computer skills).
The programme is taught in English.
Tuition Fee
-
International
5800 EUR/yearTuition FeeBased on the tuition of 5800 EUR per year during 12 months. -
EU/EEA
962 EUR/yearTuition FeeBased on the tuition of 962 EUR per year during 12 months.
Living costs for Antwerpen
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.