The TU Delft Master’s in Computer Science programme offers you the opportunity to become a computer science specialist in your favoured field within the wide spectrum of computer science. The Data Science and Technology track will let you analyse and interact with large amounts of data. The programme uses innovative educational methods, focuses on project work, and requires a strong commitment from you. You’ll be challenged to deal with a workload of more than 40 hours per week.
You will compose your individual study programme, which will consist of:
- Common Core Courses (choose 4 out of 9)
- Computer Science Courses (up to 40 EC)
- Seminar/literature study (5/10 EC)
- Free electives (≤ 25 EC) Examples: courses offered by another university/department/faculty, internship.
- Thesisproject (45 EC)
You build up your individual study programmes according to these guidelines, and can choose for either a broader orientation or an in-depth specialization. You have a lot of freedom to put together your own programme. During the master programme, you get in touch with a variety of innovative and effective educational methods. The programme consist of practical and theoretical work.
A few example subjects
Discovering important data patterns in huge medical data streams plays a vital role in the healthcare system. Examples of these data streams include large amounts of molecular data based on next generation sequencing, medical imaging like MRI, X-rays, CT or PET scans, and sensory data measuring health activity like motion or sleeping patterns. To unlock this data, you will learn e.g., to develop pattern recognition algorithms for associating DNA sequence data with diseases, multimedia search and retrieval techniques to gather relevant data, and to visualise medical data sets in a comprehensible way.
Unlocking Energy-relevant Data concerns the gathering and processing of relevant data from smart meters, solar cells, windmills, electric cars, intelligent houses and power plants, etc. to optimise our usage of energy. You will learn to develop algorithms for recognition of energy usage patterns, user modelling techniques for personalisation and preference elicitation, (multi-modal) data fusion algorithms to integrate data, and visualisation techniques to inform human operators.
Unlocking Infrastructure Operation Data concerns collecting, storing, and analysing data on the operation of various infrastructures ranging from transportation infrastructures, infrastructures for utilities, telecommunications, and the Internet. You will learn to design algorithms for data analysis, for analysing networks to optimise them or to make them more robust, for detecting the behaviours and preferences of infrastructure users, and for recognizing features from surveillance videos.
Unlocking Environment-relevant Data concerns the gathering, storing, enriching, analysing, and applying data on the interaction that humans have with their living environment. Examples of the societal challenges that we are facing include climate-adaptive buildings and cities, water management in urban environments, and improved safety in our delta regions. To work as a data scientist on these challenges, you will learn techniques and algorithms for the collection, integration and enrichment of data (from sensors, humans and infrastructures), and for modelling, simulation and analysis. In addition, you will learn how to design and implement software and information architectures for performing this type of analysis.
The Data Science & Technology track offers students freedom in choosing subjects and specialisations. Eventually, after this broad orientation, you will join one of the research groups for your specialization and thesis. Some examples of projects and topics that graduates have specialised in during their studies are:
- A music recommendation system that recognises the user's context and automatically recommends suitable music.
- The NIPT test. A new test for detecting abnormalities in the foetus during pregnancy. An algorithm can determine whether a trisomy is present in the DNA. The test has been used in Dutch hospitals for the past three years and is less dangerous to the foetus than the previously used chorionic villus sampling method.
- A software system for luggage on the conveyor belts at Schiphol. The system recognises images and the size of the luggage, and can make decisions based on this information: it automatically checks for damage and abnormal shapes and pre-sorts the 'abnormal cases'.
- Medical image processing: the recognition of blood vessels or tumours in an image, or recognising whether heart valves open and close in the correct manner which reveals how fit someone is.
The thesis project is the last study unit of the programme and serves to prove that you acquired the academic competences of a Master of Science in Computer Science. The project involves a research or design task with sufficient academic level.
The project may be executed within a research programme at the TU Delft, or in collaboration with a company or research institute. The student executes the thesis project independently, with guidance of a thesis supervisor and under the responsibility of a full professor of one of the researchgroups involved in the Computer Science programme.
Students conduct their thesis project under the supervision of one of the Computer Science research groups. The number of students a specific research group can supervise is limited. Therefore, you will be encouraged and coached early on in the program to think about your specific interests and finding a match between you and a research group.
|Computer Graphics and Visualisation|
|Network Achitectures and Services|
|Pattern recognition and Bioinformatics|
|Web Information Systems|
Some examples of recent graduation projects:
- A Workload Model for MapReduce, by Thomas de Ruiter, 2012
- “Driving engagement and online social behaviour of employees in an enterprise environment”, Catalin Stanculescu
- HiveArcs - Visualizing genome co-expression data by Lennaert van den Brink, 2014
- Negotiation support to humans for multi-party negotiations.
- Detection of Fetal Copy Number Aberrations by Shallow Sequencing of Maternal Blood Samples, Roy Straver, by Straver, R. 2012.
Extra Curricular Activities
Although students of the master programme Data Science and Technology are already heading to great careers, you can make an extra mile by taking up the challenge of the Honours Programme. This programme is especially for you if you are an excelling student and you want to enrich and deepen your knowledge even further. The course doesn’t only improve your skills, but also contributes to career opportunities in the future as it is a proof of excelling at the university. Due to the interdisciplinary character of this programme you’ll collaborate with students from other faculties.