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 Thilo Stadelmann (ZHAW)
Reposted from https://dublin.zhaw.ch/~stdm/?p=350#more-350
I recently came about the notion of “type A” and “type B” data scientists. While the “type A” is basically a trained statistician that has broadened his field towards modern use cases (“data science for people”), the same is true for “type B” (B for “build”, “data science for software”) that has his roots in programming and contributes stronger to code and systems in the backend.
Frankly, I haven’t come about a practically more useless distinction since the inception of the term “data science”. Data science is the name for a new discipline that is in itself interdisciplinary [see e.g. here – but beware of German text]. The whole point of interdisciplinarity, and by extension of data science, is for proponent to think outside the box of his or her original discipline (which might be be statistics, computer science, physics, economics or something completely different), and acquire skills in the neighboring disciplines in order to tackle problems outside of intellectual silos. Encouraging practitioners to stay in their silos, as this A/B typology suggests, is counterproductive at best, fatal at worst. 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
The Swiss Data Science community recently met at SDS|2015, the 2nd Swiss Workshop on Data Science. It was a full day event organized by ZHAW Datalab, with inspiring talks, hands-on data expeditions, and an excellent provision of space and atmosphere for fruitful networking. The conference took place on the 12th of June at the premises of ZHAW in Winterthur. It attracted people with a wide range of skills, expertise, and levels from doers to managers, and had very strong support from industry, hence showing the huge potential and scope of the subject.
After having the workshop kicked-off by the President of ZHAW, Dr. Jean Egbert Sturm, Professor of Microeconomics & Director of KOF Swiss Economic Institute at ETH, gave an insightful keynote talk on The use of ever increasing datasets in Macroeconomic forecasting. He explained to the audience the way to do economic forecasting using simple and standard analytical techniques. It was specifically very interesting for data analytics experts to see such a methodology that successfully uses down-to-earth analytical techniques integrated with in-depth knowledge of Economics. Continue reading
Drew Conway´s data science Venn diagram is used by many (including me) to give a first impression of what data science is all about. And rightly so: I, for example, like it for its simplicity and “coolness”.
When in a more in-depth discussion, moving from mere buzz to concrete skills and project possibilities, we at the Datalab have gained good experiences with the following “skill set map”:
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.