Introduces the Radboud University's Data Science specialisation of the Computer Science Master.

1. Data Science Master Specialisation
Arjen P. de Vries and Tanja Crijns

2. Data
Scientist

3. Scientific Questions
• What is clustering?
• What is causality?
• What’s the magic behind deep learning?
• How can you efficiently search and rank?
• How do you build reliable models from complex data?

4. Why are these questions important?
To help and improve our society

5. iCIS data science groups
• Prof. Tom Heskes
machine learning theory and applications
• Prof. Elena Marchiori
complex networks and machine learning
• Prof. Arjen P. de Vries
information retrieval and large-scale databases
• Prof. Mireille Hildebrandt
privacy and legacy aspects of data mining

6. But also…
• Prof. Nico Karssemeijer (RadboudUMC)
computer-aided diagnosis and medical imaging
• Prof. Antal van den Bosch (Faculty of Arts)
machine learning and language technology
• Prof. Lutgarde Buydens (Institute for Molecules and Materials)
chemometrics
• Prof. Bert Kappen (Donders Institute)
machine learning and stochastic control

7. Course outline
1st semester Track Basis Track Basis
Track
Choice
Track
Choice
Free Choice
2nd
semester
Track Basis
Research
Seminar
Track
Choice
Track
Choice
Free Choice
3rd
semester
Research Project
CS &
Society External
Choice
External
choice
4th
semester
Master Thesis

8. Track basis courses
Mandatory, key methodological aspects:
•Machine Learning in Practice (6 ec)
•Information Retrieval (6 ec)
•Bayesian Networks (6 ec)
Mandatory background:
•Philosophy and Ethics for Computing and Information Science (3 ec)
•Research Seminar Data Science (6ec)
Responsible Data ScienceResponsible Data Science
Deep dive into researchDeep dive into research

9. Track choice courses
• Statistical Machine Learning (6 ec)
• Natural Computing (6 ec) Theory and Tools
• Machine Learning (9 ec)
• Intelligent Systems in Medical Imaging (6 ec)
• Computational Cognitive Neuroscience (6 ec)
• Pattern Recognition for Natural Sciences (3 ec) Applications
• Text Mining (6 ec)
• Cognitive Computational Modeling of Language and Web Interaction (6 ec)
• Comparative Genomics (3ec)
• (Automatic) Speech Recognition (6 ec)
• Law in Cyberspace (6 ec)
• Foundations of Information Systems (6 ec) Other aspects
• Business Rules Specification and Application (3 ec)

10. • Join one of the research groups within iCIS/RU or do an internship at a company
- How to improve product suggestions based on global sentiments?
- Which clients of the Belastingdienst have a high risk of non-compliance?
- Can you predict penalty-free prepayment of mortgages?
- How to find micro-calcifications in breast mammography images?
- Can you predict the number of solar panels in a specific region?
Research projects

11. Master thesis projects
• Niels Radstake investigated Bayesian approaches to analyze mammographic
images
• Jelle Schühmacher came up with a classifier-based method for
searching large document collections
• Tom de Ruijter worked on his master thesis at Xerox in Grenoble
to improve dynamic pricing for parking in LA and other US cities
• Harm Berntsen did his master thesis at Nedap, using deep
learning to automatically segment screenshots of user interfaces

12. Master thesis projects
• Tim Janssen developed novel methods for predicting the workforce needed in retail
stores
• Bas van Berkel made clever use of Wikipedia to build a
system for automatically translating words
• Steven Reitsma applied deep learning to visual question
answering
• Harmen Prins investigated the use of word embeddings for ontology matching
• Farhad Zanjani improved convolutional neural networks for
semantic video integration

13. Data Science vs Web and Language Interaction
• Overlap: text mining, information retrieval, machine learning in practice, ...
• Data Science: broader scope of application domains, more emphasis on
methodological aspects
• Web and Language Interaction: dive deeper into the
psychological and neurological aspects of
human-human and human-computer interaction
• “Process school” vs. “product school” view on AI
• Can do both through a joint, double master program:
180 ec (3 years) for 2 master degrees!

15. Archive Otto-Lilienthal-Museum / www.lilienthal-museum.de

16. Job perspective
• Start your own company in data analytics, become a data analysis specialist or
consultant at a larger company, or go for a PhD
Steven Reitsma
Data Scientist at Big Data Republic
Bas van Berkel
Data Scientist at Achmea
Kristel Rösken
Business analyst
at VVV Nederland
Harmen Prins
Big Data Specialist at Deloitte
Alex Slatman
Director at OBI4wanFarhad Zanjani
PhD student at TU/e
Tom de Ruijter
Data Scientist at MeteoConsult
Niklas Weber
Data Scientist at Booking.com

17. Why Data Science at the Radboud University?
• Diversity: various aspects and applications of data science
• Flexibility: large choice of courses to shape
student interests
• Excellence: students are embedded in
research groups

18. Admission requirements
•A Bachelor’s diploma in
- Computing Science
- Artificial Intelligence
- Related studies (Science, Physics, Math, ...), possibly with a few additional
courses
•Specific pre-master’s for students with a diploma from the university of applied
sciences (HBO)
•Fluency in both written and spoken English
•Check out with our student advisor for details or in case of doubt

20. Why did I choose Data Science
• Background
- Artificial Intelligence BSc
• Data Science related courses
- Data mining
- Neural networks
• Bachelor thesis on text categorization
- Assigning one of 10 categories to an event based
on a description
Music? Dance?
Theater?
Movie?

21. Example: Text Mining
Different areas of text mining:
•Text categorization
•Summarization
•Sentiment analysis
•Question answering
•Topic modeling

22. Example: Intelligent Systems in Medical Imaging
• Basic methods of image processing work
• The role of machine learning in medical imaging
• Apply Deep Learning
• Implement and evaluate a system for a clinical application

23. Example: Machine Learning in Practice
• Participate in 2 machine learning challenges
- Groups of 5
• The Nature Conservancy Fisheries Monitoring
- Classify species of fish
• Work with actual data distributed by companies
and organizations
• Real prize pool

24. My impressions
• Is it fun?
• Is it difficult?
• What do you like the most about it?
• For questions about my experience with courses or anything else; come find me afterwards!
Yes!
Sometimes!
That I’m developing a set of hard skills that are useful in the
academic world ánd the industry

25. Questions?
Arjen P. de Vries Tanja Crijns
arjen@science.ru.nl tanja987.crijns@student.ru.nl
024 - 365 2354
Or, contact the student advisor:
Perry Groot
024 - 365 2037
perry@cs.ru.nl