DoctorateFaculty of Social Sciences & Humanities

Humanities Data Science (Ph.D.)

Willkommen! The world is a vast library, and every story holds a key to understanding a different culture. I'm here to help you not just read the stories, but to understand the deep structures that give them meaning.

Meet the academic team Explore the faculty

Identity only. No score is printed. Checkout waits.

Level: Doctorate
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

NXAcademic
Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Digital Humanities, Computational Linguistics, Network Analysis, Data Visualization

Practical focus

Digital Humanities, Computational Linguistics, Network Analysis, Data Visualization

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

The British Library
Internships in digital archives and preservation
The Guggenheim Museum
Opportunities in digital humanities and curation
The National Archives
Internships in archival science

Career opportunities

Digital Archivist
In a museum or library
Historian
In academia or a research institution
Data Analyst
For a government agency or research center
Documentarian
Creating historical documentaries

Jobs and projects

Critical Thinking
Evaluating the validity and reliability of humanities research
Research Skills
Finding, analyzing, and synthesizing information
Programming
Competency in languages like Python or R
Communication
Explaining complex humanities concepts clearly

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- You will gain a deep understanding of how to use data to improve human health, with a skill set that is in high demand in the rapidly growing field of personalized medicine and diagnostics.
Skills you build
- Digital Humanities: Using computational tools to research the humanities.
- Computational Linguistics: Analyzing language with computational methods.
- Network Analysis: Understanding the structure and dynamics of social networks.
- Data Visualization: Creating compelling visual representations of humanities data.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Humanities Data Science (Ph.D.)

01Advanced Digital Humanities
1. FoundationsFoundations of Advanced Digital Humanities
  The learner can you will gain a deep understanding of how to use data to improve human health, with a skill set that is in high demand in the rapidly growing field of personalized medicine and diagnostics, as applied to Advanced Digital Humanities.
  The learner can distinguish related ideas inside Advanced Digital Humanities.
2. MethodsMethods in Advanced Digital Humanities
  The learner can apply a method from Advanced Digital Humanities to a documented case.
  The learner can select an appropriate method from Advanced Digital Humanities for a stated problem.
3. ApplicationApplication of Advanced Digital Humanities
  The learner can evaluate a practice of Advanced Digital Humanities against a stated criterion.
  The learner can transfer Advanced Digital Humanities to a new documented context.
02Computational Linguistics and Text Analysis
1. FoundationsFoundations of Computational Linguistics and Text Analysis
  The learner can explain the core terms of Computational Linguistics and Text Analysis.
  The learner can distinguish related ideas inside Computational Linguistics and Text Analysis.
2. MethodsMethods in Computational Linguistics and Text Analysis
  The learner can apply a method from Computational Linguistics and Text Analysis to a documented case.
  The learner can select an appropriate method from Computational Linguistics and Text Analysis for a stated problem.
3. ApplicationApplication of Computational Linguistics and Text Analysis
  The learner can evaluate a practice of Computational Linguistics and Text Analysis against a stated criterion.
  The learner can transfer Computational Linguistics and Text Analysis to a new documented context.
03Network Analysis for Humanities
1. FoundationsFoundations of Network Analysis for Humanities
  The learner can explain the core terms of Network Analysis for Humanities.
  The learner can distinguish related ideas inside Network Analysis for Humanities.
2. MethodsMethods in Network Analysis for Humanities
  The learner can apply a method from Network Analysis for Humanities to a documented case.
  The learner can select an appropriate method from Network Analysis for Humanities for a stated problem.
3. ApplicationApplication of Network Analysis for Humanities
  The learner can evaluate a practice of Network Analysis for Humanities against a stated criterion.
  The learner can transfer Network Analysis for Humanities to a new documented context.
04Research Seminar in Digital Humanities
1. FoundationsFoundations of Research Seminar in Digital Humanities
  The learner can explain the core terms of Research Seminar in Digital Humanities.
  The learner can distinguish related ideas inside Research Seminar in Digital Humanities.
2. MethodsMethods in Research Seminar in Digital Humanities
  The learner can apply a method from Research Seminar in Digital Humanities to a documented case.
  The learner can select an appropriate method from Research Seminar in Digital Humanities for a stated problem.
3. ApplicationApplication of Research Seminar in Digital Humanities
  The learner can evaluate a practice of Research Seminar in Digital Humanities against a stated criterion.
  The learner can transfer Research Seminar in Digital Humanities to a new documented context.
05The Ethics of AI in Humanities Research
1. FoundationsFoundations of The Ethics of AI in Humanities Research
  The learner can explain the core terms of The Ethics of AI in Humanities Research.
  The learner can distinguish related ideas inside The Ethics of AI in Humanities Research.
2. MethodsMethods in The Ethics of AI in Humanities Research
  The learner can apply a method from The Ethics of AI in Humanities Research to a documented case.
  The learner can select an appropriate method from The Ethics of AI in Humanities Research for a stated problem.
3. ApplicationApplication of The Ethics of AI in Humanities Research
  The learner can evaluate a practice of The Ethics of AI in Humanities Research against a stated criterion.
  The learner can transfer The Ethics of AI in Humanities Research to a new documented context.
06The Practical Guide to Digital History
1. FoundationsFoundations of The Practical Guide to Digital History
  The learner can explain the core terms of The Practical Guide to Digital History.
  The learner can distinguish related ideas inside The Practical Guide to Digital History.
2. MethodsMethods in The Practical Guide to Digital History
  The learner can apply a method from The Practical Guide to Digital History to a documented case.
  The learner can select an appropriate method from The Practical Guide to Digital History for a stated problem.
3. ApplicationApplication of The Practical Guide to Digital History
  The learner can evaluate a practice of The Practical Guide to Digital History against a stated criterion.
  The learner can transfer The Practical Guide to Digital History to a new documented context.
07Archival Science for Public Policy
1. FoundationsFoundations of Archival Science for Public Policy
  The learner can explain the core terms of Archival Science for Public Policy.
  The learner can distinguish related ideas inside Archival Science for Public Policy.
2. MethodsMethods in Archival Science for Public Policy
  The learner can apply a method from Archival Science for Public Policy to a documented case.
  The learner can select an appropriate method from Archival Science for Public Policy for a stated problem.
3. ApplicationApplication of Archival Science for Public Policy
  The learner can evaluate a practice of Archival Science for Public Policy against a stated criterion.
  The learner can transfer Archival Science for Public Policy to a new documented context.
08Capstone Project in Historical Analysis
1. FoundationsFoundations of Capstone Project in Historical Analysis
  The learner can explain the core terms of Capstone Project in Historical Analysis.
  The learner can distinguish related ideas inside Capstone Project in Historical Analysis.
2. MethodsMethods in Capstone Project in Historical Analysis
  The learner can apply a method from Capstone Project in Historical Analysis to a documented case.
  The learner can select an appropriate method from Capstone Project in Historical Analysis for a stated problem.
3. ApplicationApplication of Capstone Project in Historical Analysis
  The learner can evaluate a practice of Capstone Project in Historical Analysis against a stated criterion.
  The learner can transfer Capstone Project in Historical Analysis to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

I am a digital humanist who believes that the most complex systems we will ever study are human cultures. My work focuses on using computational methods to research, preserve, and present the past, ensuring that our historical knowledge is as dynamic and accessible as possible.

Applied mentorship

I am a digital humanist with a passion for using data to make real-world impacts in academia and public policy. My mentorship focuses on helping students apply their skills to clinical and research problems, turning raw data into actionable insights.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

• Blog Post: 'The Algorithm of Memory: How AI is Changing History' - An accessible essay on the use of AI and machine learning to analyze historical documents and artifacts. · • Blog Post: 'Why Every Historian Needs to Be a Data Scientist' - Discusses the importance of digital skills for historians and the future of the field. · • Conference Paper: 'A Network Analysis of the Silk Road Trade Routes' - Presented at the International Conference on Digital Humanities, detailing a new method for analyzing historical trade networks. · • Journal Article: 'The Role of Digital Archives in Post-Conflict Reconciliation' - Published in the Journal of Archival Science, this paper provides a framework for using digital archives to aid in post-conflict recovery. · • Standard Article: 'A Digital Tour of Ancient Roman Civilizations' - A multi-part series for a history magazine on how digital tools can be used to recreate and explore ancient sites. · • Book: 'The Digital Archivist: A Historian's Guide' - A comprehensive textbook on the theoretical and practical applications of digital archives. · • Total Score: 28/30 · • Kairos Badge: 🥇

R / 02

Mentor practice lens

• Article: 'The Role of Digital Archives in Preserving Cultural Heritage' - A concise article on how digital archives can be used to preserve and protect cultural heritage. · • Guide: 'A Beginner's Guide to Using R for Historical Data Analysis' - A practical tutorial for new students on the basics of a key bioinformatics programming language. · • Essay: 'Building Ethical AI for Public Policy' - An essay on the challenges and responsibilities of creating AI tools for government. · • Total Score: 28/30 · • Kairos Badge: 🥇

Adaptive capability

Professor superpower

GAF-powered Historical Data Visualization

Adaptive capability

Mentor superpower

GAF-powered Clinical Insight Extraction

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Stefan Meyer

Digital Humanities, Computational Linguistics, Network Analysis, Data Visualization

Meet your professor Open the classroom

AI Super Mentor

Dr. Antoine Girard

Digital Humanities, Computational Linguistics, Network Analysis, Data Visualization

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Adapted. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master	Doctorate This programme
9 months · Fast track	12000 EUR	9600 EUR	12000 EUR
12 months · Recommended	14400 EUR	12000 EUR	14400 EUR
15 months · Standard	16800 EUR	14400 EUR	16800 EUR
18 months · Flexible	19200 EUR	16800 EUR	19200 EUR
21 months · Extended	21600 EUR	19200 EUR	21600 EUR
24 months · Part-time	24000 EUR	21600 EUR	24000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

Named tuition lists Add-on services

Continue exploring

Find the program that expands your universe.

Browse all programs

Humanities Data Science (Ph.D.)

Academic focus

Practical focus

The British Library

The Guggenheim Museum

The National Archives

Digital Archivist

Historian

Data Analyst

Documentarian

Critical Thinking

Research Skills

Programming

Communication

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Stefan Meyer

Dr. Antoine Girard

Find the program that expands your universe.