The aim of the PhD Network in Data Science is to offer students with a master degree (including degrees from an university of applied sciences) the opportunity to obtain a PhD in cooperation between a university of applied sciences and a university.
The PhD Network in Data Science is supported by Swissuniversities. It is a cooperation between three departments of ZHAW Zurich University of Applied Sciences (School of Management and Law, Life Science and Facility Management, School of Engineering), three departments of the University of Zurich (Faculty of Science, Faculty of Business, Economics and Informatics, Faculty of Arts and Social Sciences), the Faculty of Science at the University of Neuchatel and the Department of Innovative Technologies at SUPSI University of Applied Sciences and Arts of Southern Switzerland.
PhD students work in applied research projects at the university of applied sciences and are supervised jointly by a supervisor at the university and a co-supervisor at the university of applied sciences. They are enrolled in the regular PhD programs of the partner universities and have to go through the standard admission procedure. After successful completion they receive the doctorate of the respective partner university (UZH or UNIBE). The PhD Network is also open to students with a master’s degree from a university of applied sciences. They, however, have to go through convergence programs (specific to the respective faculty) for admission to the partner universities.
You can find more information on our new website!
By Kurt Stockinger (ZHAW)
The final results of an interdisciplinary study funded by „TA Swiss“ on „Quantified Self“ with participation of the Datalab have been published. The study was performed by three ZHAW departments (School of Health Professions, School of Management and Law, School of Engineering) in cooperation with the Institute for Futures Studies and Technology Assessment, Berlin. The focus of the Datalab was on legal and Big Data aspects of quantified self.
The results are available in various forms:
- A book (for people who love reading)
- A 24-page summary in four languages (for people who don’t want to read some 250 pages)
- A podcast from SRF 1 (Echo der Zeit)
- A NZZ article
Enjoy reading and maybe you get encouraged to “quantify yourself” a bit better 😉
by Thilo Stadelmann (ZHAW)
In 2014, ZHAW Datalab started the SDS conference series. It was the year with only one Swiss data scientist identifiable on LinkedIn (at Postfinance…). The year where we talked about “Big Data”, and not “Digitization”. The year where we were unsure if such a thing as a Swiss data science community would exist, and if it actually would come to such an event.
SDS grew from a local workshop to a conference with over 200 participants and international experts as keynote speakers in 2016. This was the year where finally a Swiss-wide network of strong partners form academia and industry emerged to push innovation in data-driven value creation: the Swiss Alliance for Data-Intensive Services (www.data-service-alliance.ch). We as datalabbers have been instrumental in founding this alliance, and then found it to be the perfect partner to take this event to the next level of professionalism.
By Gabriel Eyyi (ZHAW)
In this blog post I will show how to combine dynamic models from Modelica with reinforcement learning.
As part of one of my master projects a software environment was developed to examine reinforcement learning algorithms on existing dynamic models from Modelica in order to solve control tasks. Modelica is a non-proprietary, object-oriented, equation based language to conveniently model complex physical systems .
The result is the Python library Dymola Reinforcement Learning (dymrl) which allows you to explore reinforcement learning algorithms for dynamical systems.
The code of this project can be found at github.
By Amrita Prasad (ZHAW)
It’s already been a month since we met as the Swiss Data Science community at our 3rd Swiss Conference on Data Science (SDS|2016), pushed again by ZHAW’s Datalab group and presented by SAP Switzerland.
Several additional organisations sponsored and supported the conference to give it a successful execution – the organising committee thanks IT Logix & Microsoft, PwC, Google, Zühlke, SGAICO, Hasler Stiftung and the Swiss Alliance for Data-Intensive Services for their support in bringing together a successful event! Continue reading
By Oliver Dürr (ZHAW)
Reposted from http://oduerr.github.io/blog/2016/04/06/Deep-Learning_for_lazybones
In this blog I explore the possibility to use a trained CNN on one image dataset (ILSVRC) as feature extractor for another image dataset (CIFAR-10). The code using TensorFlow can be found at github. Continue reading
In this article, I recount measures and approaches used to deal with a relatively small data set that, in turn, has to be covered “perfectly”. In current academic research and large-scale industrial applications, datasets contain millions to billions (or even more) of documents. While this burdens implementers with considerations of scale at the level of infrastructure, it may make matching comparatively easy: if users are content with a few high quality results, good retrieval effectiveness is simple to attain. Larger datasets are more likely to contain any requested information, linguistically encoded in many different ways, i.e., using different spellings, sentences, grammar, languages, etc.: a “blind shot” will hit a (one of many) target.
However, there are business domains whose main entities’ count will never reach the billions, therefore inherently limiting the document pool. We have recently added another successfully finished CTI-funded project to our track record, which dealt in such a business domain. Dubbed “Stiftungsregister 2.0”, the aim of the project was to create an application which enables users to search for all foundations in existence in Switzerland.
I’m glad that Thilo mentioned Security & Privacy as part of the data science skill set in his recent blog post. In my opinion, the two most interesting questions with respect to security & privacy in data science are the following:
- Data science for security: How can data science be used to make security-relevant statements, e.g. predicting possible large scale cyber attacks based on analysing communication patterns?
- Privacy for data science: how can data that contains personal identifiable information (PII) be anonymized before providing them to the data scientists for analysis, such that the analyst cannot link data back to individuals? This is typically identified with data anonymization.
This post deals with the second question. I’ll first show why obvious approaches to anonymize data typically don’t offer true anonymity and will then introduce two approaches that provide better protection.
SODA (Search over Data Warehouse) provides a Google-like search interface for querying an enterprise data warehouse. The tool enables non-tech savvy users, who do not have technical knowledge of the underlying database system or the query language SQL, to intuitively explore complex data warehouses. The main idea is to use metadata information about the data model as well as inverted indexes about the base data to generate executable SQL. SODA thus combines methods from database systems, information retrieval and semantic web technology to enable self-service business intelligence.
SODA was originally developed as a joint research project between Credit Suisse and ETH Zurich as part of the Enterprise Computing Center (http://www.ecc.ethz.ch/research/semdwhsearch). At Zurich University of Applied Sciences we will continue the research jointly with ETH Zurich.