10th Swiss Big Data User Group Meeting

Monday, 3rd February 2014 at 5pm - 8pm

Location: ETH Zurich, building CHN, room F46, Universitätstrasse 16, 8092 Zurich

This event is in the past.

This event will be dedicated to "Data Science". 

Map: http://www.mapsearch.ethz.ch/map/mapSearchPre.do?gebaeudeMap=CHN&farbcode=c070
Begin: 6:00pm

Agenda

Educating Data Scientists of the Future 
Dr. Kurt Stockinger, Zurich University of Applied Sciences

Many companies are struggling with Big Data. Some argue that Big Data is the new answer to all problems while others are more critical about it. What is common to many discussions with IT professionals is that almost everyone has a different understanding of the topic. Moreover, many enterprises find it very hard to recruit the perfect data scientist to solve Big Data problems.

In this talk we give an overview of our understanding of data science and present the driving factors for the newly established Datalab at Zurich University of Applied Sciences. The goal of the lab is to establish a sound curriculum and research agenda to prepare data scientists for the ever-increasing demand from industry and to allow industry partners collaborate with academia to solve problems that go beyond everyday routines.


Big Data and Data Science for traditional Swiss companies

Dr. Daniel Fasel, CEO Scigility AG

Many traditional Swiss companies, such as banks, insurance companies and government agencies, are highly interested in Big Data and Data Science but don’t know exactly what the business value of Big Data is for them. Often Big Data is misinterpreted as large amounts of data and companies are unaware of the innovation behind the new technologies of Big Data and how these technologies can be profitable to them. In this presentation, I discuss sample cases that demonstrate a set of these new technologies and how they can be applied not only for large web scale data but also for data sets of traditional companies. First, I demonstrate how multi-structured data can be indexed and searched using Autonomy. I show how fast new analytical application can be built based on a real-time streaming example using STORM, Redis and Node.js. And the last demonstration shows how machine learning algorithms and visualization can be applied for improving analytics using AsterData.


Design Patterns for Large-Scale Real-Time Learning

Sean Owen, Director Data Science, Cloudera

Having collected Big Data, organizations are now keen on data science and “Big Learning”. Much of the focus has been on data science as exploratory analytics: offline, in the lab. However, building from that a production-ready large-scale operational analytics system remains a difficult and ad-hoc endeavor, especially when real-time answers are required. Design patterns for effective implementations are emerging, which take advantage of relaxed assumptions, adopt a new tiered "lambda" architecture, and pick the right scale-friendly
algorithms to succeed. Drawing on experience from customer problems and the open source Oryx project at Cloudera, this session will provide examples of operational analytics projects in the field, and present a reference architecture and algorithm design choices for a successful implementation.


Networking apero sponsored by Scigility


 


About the sponsors

Big thanks to Scigility for sponsoring food and drinks and Teralytics for providing the location!

Comments

Displaying 1 comment
at 2:52pm on Monday, 6th January 2014
With best regards,
Yours sincerely.
Rainer Schaub

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