Data Science & Technology is a specialisation offered by the Master’s programme in Computer Science at the University of Twente.
Information technology is becoming increasingly embedded in our society. This also means it is getting easier for us to collect more data about ourselves and our environment. The science concerned with the ‘discovery’ of information from large volumes of unstructured data is called ‘data science’. This field has high practical relevance, as the generation and application of information is an important economic activity in today’s world. For example, data science techniques can be used in information systems for maintaining an information model of the dynamic environment, based on things like real-time sensor data. These information models, in turn, can be used to offer tailored services to the users in the environment. Isn’t that smart?
The specialization provides basic courses that will help you understand data science, the mathematics behind data science, smart services, and how these fields are related through modern information systems. The specialization also offers advanced courses in which you will be equipped to tackle the challenges of this cross-disciplinary field, including big data processing, real-time analytics, information quality, and information system and service design. These challenges include big data, real-time analytics, data modelling and smart information use.
Scientific and economic progress is increasingly powered by our capabilities to explore big data sets. A key challenge in data science is to find ways of using big data sets of varying quality that are readily available, instead of small datasets that require careful, manual work. As a student participating in this specialization, you will work with data created every hour, minute, second and millisecond, rather than data that require (laborious) manual annotation and manual cleaning. These big data sets are typically acquired by the unobtrusive monitoring of large populations of users in an everyday setting – rather than by the monitoring of small groups of carefully selected subjects in a laboratory setting. Data acquired by unobtrusive monitoring can be used in information systems to make a variety of smart services available, based on real-time data analytics, complex event processing and context-aware process adaptation.
The methodological challenges of big data analysis and smart services come with a number of technical challenges, and the need for developing new methods, models and tools. The challenges are:
- Processing data sets that are too big to be handled by a single machine or by traditional tools within a reasonable amount of time;
- Processing streaming data for real-time monitoring and tracking of events and real-time identification of trends;
- Extracting reliable conclusions and models from unreliable data, and from data integrated from multiple sources of varying quality;
- Combining the above in smart services that bring added value to end users at the right time and at the right place.
This specialization connects the important fields of data science and smart services via information systems. With regard to data science, you will learn how to dig for value in data by analysing different data sources. You will also familiarize yourself with data science algorithms from a more fundamental, mathematical perspective. With regard to smart service engineering, you will learn how to design services that effectively use system capabilities to satisfy dynamic user needs and requirements. Information systems that can use the results of data science to get more value out of data may turn current services into smart services. Already, we can see many applications of this in pervasive health, well-being, compliance management, intelligent transportation, logistics, business intelligence etc.
The Data Science & Technology Master’s specialization at the University of Twente distinguishes itself from similar specializations at other universities in several ways:
- A unique combination of expertise in computer science, data science, and service science;
- Collaboration with leading international companies, like Google, Twitter and Yahoo;
- A local infrastructure for the analysis of very large datasets, accessible to students;
- Challenging big data and data analytics applications in smart services for pervasive health, logistics, and other areas.
One of the examples of Data Science research at the UT dates from 2014 when the UT was awarded one of the Twitter Data grants. The research project that won the grant centred on the diffusion process and effectiveness of early cancer detection campaigns. The proposal was to analyse popular Twitter campaigns covering different types of cancer and geographical scopes, such as Mamming (breast cancer), Movember (prostate cancer), DaveDay (pancreatic cancer) and HPVReport (cervical cancer). The aim was to map the diffusion process in detail by determining key events and actors that accelerate the diffusion process. Social network analysis was conducted with the aim of finding out whether and when these campaign lead to word-of-mouth discussion, promotion and responses. Another aim was to assess the effectiveness of the campaigns by comparing the frequency and sentiment of mentions of a particular type of cancer (e.g. breast cancer in the case of Mamming) before and after the campaign.