Data Science Master Specialisation

Data Science Master SpecialisationArjen P. de Vries and Tanja Crijns

DataScientist

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?

Why are these questions important?

To help and improve our society

iCIS data science groups

• Prof. Tom Heskesmachine learning theory and applications

• Prof. Elena Marchioricomplex networks and machine learning

• Prof. Arjen P. de Vriesinformation retrieval and large-scale databases

• Prof. Mireille Hildebrandtprivacy and legacy aspects of data mining

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

Course outline

1st semester Track Basis Track BasisTrack

ChoiceTrack

ChoiceFree Choice

2nd semester

Track BasisResearch Seminar

Track Choice

Track Choice

Free Choice

3rd semester

Research ProjectCS &

Society External Choice

External choice

4th semester Master Thesis

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 Science

Deep dive into research

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)

• 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

Master thesis projects

• Niels Radstake investigated Bayesian approaches to analyze mammographic images

• Jelle Schühmacher came up with a classifier-based method forsearching large document collections

• Tom de Ruijter worked on his master thesis at Xerox in Grenobleto improve dynamic pricing for parking in LA and other US cities

• Harm Berntsen did his master thesis at Nedap, using deeplearning to automatically segment screenshots of user interfaces

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 asystem for automatically translating words

• Steven Reitsma applied deep learning to visual questionanswering

• Harmen Prins investigated the use of word embeddings for ontology matching

• Farhad Zanjani improved convolutional neural networks forsemantic video integration

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 thepsychological and neurological aspects ofhuman-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!

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

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 ReitsmaData Scientist at Big Data Republic

Bas van BerkelData Scientist at Achmea

Kristel RöskenBusiness analystat VVV Nederland

Harmen PrinsBig Data Specialist at Deloitte

Alex SlatmanDirector at OBI4wanFarhad Zanjani

PhD student at TU/e

Tom de RuijterData Scientist at MeteoConsult

Niklas WeberData Scientist at Booking.com

Why Data Science at the Radboud University?

• Diversity: various aspects and applications of data science

• Flexibility: large choice of courses to shapestudent interests

• Excellence: students are embedded inresearch groups

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

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 descriptionMusic? Dance?

Theater?Movie?

Example: Text Mining

Different areas of text mining:

•Text categorization

•Summarization

•Sentiment analysis

•Question answering

•Topic modeling

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

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

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

Questions?

Arjen P. de Vries Tanja Crijnsarjen@science.ru.nl tanja987.crijns@student.ru.nl 024 - 365 2354

Or, contact the student advisor:

Perry Groot024 - 365 2037perry@cs.ru.nl