Im Kontext von IoT spielt die Gewinnung und Verarbeitung von großen Datenmengen, z.B. von Sensoren eine große Rolle. Die Rohdaten alleine machen aber noch lange keine smarten Systeme. Aus Daten werden Informationen aus Informationen wird Wissen und aus Wissen resultieren Entscheidungen - im besten Fall. Neben der technischen Herausforderungen im Umgang mit BigData rückt die „schlaue Auswertung" derselben (Digitale Analyse) immer mehr in den Vordergrund und zeigt die Grenzen des Könnens vieler Unternehmen auf. Kein Wunder also, dass dem Berufsbild des Data Scientisten eine wachsende Bedeutung zukommt. Nicht umsonst benannte das Harvard Business Review diesen als „The sexiest job of the 21st Century“.
Die Digital Analytics Assocations e.V. (DAA) treibt gezielt Fach- und Führungskräfte sowie Unternehmen die Professionalisierung von Digitalen Analysten und Data Scientists voran.
Frank Pörschmann, Mitglied des Vorstands des DAA e.V., erzählt in diesem Vortrag etwas über
- den Unterschied zwischen BigData, SmartData und Data Analytics
- Datenökonomie
- das Berufsbild des Data Scientist / Digitalen Analysten
- Aus- und Fortbildungsmöglichkeiten
With approximately 1.x years of delay to the US, the term "Data Science" is also gaining speed in Europe. We see more and more job openings for- and business cards of data scientists, new events dedicated to the topic and an increased demand in related education literally every month. In response to this trend, Zurich University of Applied Sciences founded the ZHAW Data Science Laboratory (Datalab) last year.
This talk is to give an updated overview of Data Science in Europe by the example of the Datalab's activities in Switzerland. After a definition and classification of the field, a presentation of real technical projects sets the stage for what Data Science looks like here, offside of internet behemoths and big data clichés. Then, conclusions on the state of the art at least in Switzerland are drawn from evaluating the recent "1st Swiss Workshop on Data Science" event and ZHAW's professional education programme "DAS in Data Science".
With the help of the audience during the subsequent discussion, these results can eventually be extrapolated to the wider European community.
How Data Science Builds Better Products - Data Science Pop-up SeattleDomino Data Lab
Data Science and Big Data are ushering in a new era in adaptive applications that learn from large and varied datasets and adjust their features based on the changing environment. This talk will look at how Data Science can be successfully bridged with Big Data Architectures and Agile Software Delivery to create a new class of software that answers the demands of today's rapidly-changing enterprises. Practical techniques and real-world case studies will highlight the approaches required to successfully build these exciting new enterprise tools. Presented by Sean McClure, Ph.D. Data Scientist, Senior Consultant at ThoughtWorks.
Anhand konkreter Projekte mit Schweizer Unternehmungen stellt das Datalab der Zürcher Hochschule für Angewandte Wissenschaften die Breite und Möglichkeiten für Data Science Anwendungen im Schweizer Markt dar. Praktische Erfahrung mit potentiellen Anwendungsfällen können interessierte Unternehmen dabei im Rahmen der „Big Data Roadshow“ der Firma Serwise sammeln.
Amanda Casari, Senior Data Scientist, Concur at MLconf SEA - 5/20/16MLconf
Scaling Data Science Products, Not Data Science Teams: Congratulations! Your data science feature works! Your metrics are outstanding. Your data scientists and engineers have created useful products for your customers with proven results. Now your product and marketing teams are ready to move into new markets. Your underlying population changes. The skewed statistics of your data shifts depending on the data center you analyze. Your product is now localized and your NLP methods must adjust for a greater range of languages. Your requirements have grown. Your team has not.
How do you scale success for global products in multiple data centers with small teams?
The Data Science team at Concur’s work to grow our products into international markets has not required a global scaling of resources. This talk will share our lessons learned in creating modular, reusable data science products deployable to international, segregated data centers.
Data Science at LinkedIn - Data-Driven Products & InsightsYael Garten
Talk given at Big Boulder conference hosted by Gnip in Boulder, Colorodo on June 21, 2012. This talk provides an intro to Data Science at LinkedIn, and highlights the type of roles a Data Science team can play at a data-driven company. We use data (1) to create products that truly serve our members, (2) to derive insights, and (3) to generate wisdom which enables us to take the products and company to the next level. LinkedIn's data on 160+ million professionals' careers and networks provides a fascinating playground for data scientists to discover data insights about career trends, the social web and the economy.
Im Kontext von IoT spielt die Gewinnung und Verarbeitung von großen Datenmengen, z.B. von Sensoren eine große Rolle. Die Rohdaten alleine machen aber noch lange keine smarten Systeme. Aus Daten werden Informationen aus Informationen wird Wissen und aus Wissen resultieren Entscheidungen - im besten Fall. Neben der technischen Herausforderungen im Umgang mit BigData rückt die „schlaue Auswertung" derselben (Digitale Analyse) immer mehr in den Vordergrund und zeigt die Grenzen des Könnens vieler Unternehmen auf. Kein Wunder also, dass dem Berufsbild des Data Scientisten eine wachsende Bedeutung zukommt. Nicht umsonst benannte das Harvard Business Review diesen als „The sexiest job of the 21st Century“.
Die Digital Analytics Assocations e.V. (DAA) treibt gezielt Fach- und Führungskräfte sowie Unternehmen die Professionalisierung von Digitalen Analysten und Data Scientists voran.
Frank Pörschmann, Mitglied des Vorstands des DAA e.V., erzählt in diesem Vortrag etwas über
- den Unterschied zwischen BigData, SmartData und Data Analytics
- Datenökonomie
- das Berufsbild des Data Scientist / Digitalen Analysten
- Aus- und Fortbildungsmöglichkeiten
With approximately 1.x years of delay to the US, the term "Data Science" is also gaining speed in Europe. We see more and more job openings for- and business cards of data scientists, new events dedicated to the topic and an increased demand in related education literally every month. In response to this trend, Zurich University of Applied Sciences founded the ZHAW Data Science Laboratory (Datalab) last year.
This talk is to give an updated overview of Data Science in Europe by the example of the Datalab's activities in Switzerland. After a definition and classification of the field, a presentation of real technical projects sets the stage for what Data Science looks like here, offside of internet behemoths and big data clichés. Then, conclusions on the state of the art at least in Switzerland are drawn from evaluating the recent "1st Swiss Workshop on Data Science" event and ZHAW's professional education programme "DAS in Data Science".
With the help of the audience during the subsequent discussion, these results can eventually be extrapolated to the wider European community.
How Data Science Builds Better Products - Data Science Pop-up SeattleDomino Data Lab
Data Science and Big Data are ushering in a new era in adaptive applications that learn from large and varied datasets and adjust their features based on the changing environment. This talk will look at how Data Science can be successfully bridged with Big Data Architectures and Agile Software Delivery to create a new class of software that answers the demands of today's rapidly-changing enterprises. Practical techniques and real-world case studies will highlight the approaches required to successfully build these exciting new enterprise tools. Presented by Sean McClure, Ph.D. Data Scientist, Senior Consultant at ThoughtWorks.
Anhand konkreter Projekte mit Schweizer Unternehmungen stellt das Datalab der Zürcher Hochschule für Angewandte Wissenschaften die Breite und Möglichkeiten für Data Science Anwendungen im Schweizer Markt dar. Praktische Erfahrung mit potentiellen Anwendungsfällen können interessierte Unternehmen dabei im Rahmen der „Big Data Roadshow“ der Firma Serwise sammeln.
Amanda Casari, Senior Data Scientist, Concur at MLconf SEA - 5/20/16MLconf
Scaling Data Science Products, Not Data Science Teams: Congratulations! Your data science feature works! Your metrics are outstanding. Your data scientists and engineers have created useful products for your customers with proven results. Now your product and marketing teams are ready to move into new markets. Your underlying population changes. The skewed statistics of your data shifts depending on the data center you analyze. Your product is now localized and your NLP methods must adjust for a greater range of languages. Your requirements have grown. Your team has not.
How do you scale success for global products in multiple data centers with small teams?
The Data Science team at Concur’s work to grow our products into international markets has not required a global scaling of resources. This talk will share our lessons learned in creating modular, reusable data science products deployable to international, segregated data centers.
Data Science at LinkedIn - Data-Driven Products & InsightsYael Garten
Talk given at Big Boulder conference hosted by Gnip in Boulder, Colorodo on June 21, 2012. This talk provides an intro to Data Science at LinkedIn, and highlights the type of roles a Data Science team can play at a data-driven company. We use data (1) to create products that truly serve our members, (2) to derive insights, and (3) to generate wisdom which enables us to take the products and company to the next level. LinkedIn's data on 160+ million professionals' careers and networks provides a fascinating playground for data scientists to discover data insights about career trends, the social web and the economy.
Google Trends and other IT fever charts rate Data
Science among the most rapidly emerging and promising fields that expand around computer science. Although Data Science draws on content from established fields like artificial intelligence, statistics, databases, visualization and many more, industry is demanding for trained data scientists that no one seems able to deliver. This is due to the pace at which the field has expanded and the corresponding lack of curricula; the
unique skill set, which is inherently multi-disciplinary; and the translation work (from the US web economy to other ecosystems) necessary to realize the recognized world-wide potential of applying analytics to all sorts of data.
In this contribution we draw from our experiences in establishing an inter-disciplinary Data Science lab in order to highlight the challenges and potential remedies for Data Science
in Europe. We discuss our role as academia in the light of the potential societal/economic impact as well as the challenges in organizational leadership tied to such inter-disciplinary work.
Examples, techniques, and lessons learned building data products over the last 4 years at LinkedIn.
Pete Skomoroch is a Principal Data Scientist at LinkedIn where he leads a team focused on building data products leveraging LinkedIn's powerful identity and reputation data.
The talk describes some techniques and best practices applied to develop products like LinkedIn Skills & Endorsements.
This talk was presented at the SF Data Science Meetup on September 19th, 2013
Great data leads to great insights which leads to great products.
Vitaly Gordon, senior products data scientist, talks about the culture, people and tools that have helped LinkedIn become the world’s leading professional social network and one of the most visited sites on the web.
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42
Google Trends and other IT fever charts rate Data
Science among the most rapidly emerging and promising fields that expand around computer science. Although Data Science draws on content from established fields like artificial intelligence, statistics, databases, visualization and many more, industry is demanding for trained data scientists that no one seems able to deliver. This is due to the pace at which the field has expanded and the corresponding lack of curricula; the
unique skill set, which is inherently multi-disciplinary; and the translation work (from the US web economy to other ecosystems) necessary to realize the recognized world-wide potential of applying analytics to all sorts of data.
In this contribution we draw from our experiences in establishing an inter-disciplinary Data Science lab in order to highlight the challenges and potential remedies for Data Science
in Europe. We discuss our role as academia in the light of the potential societal/economic impact as well as the challenges in organizational leadership tied to such inter-disciplinary work.
Examples, techniques, and lessons learned building data products over the last 4 years at LinkedIn.
Pete Skomoroch is a Principal Data Scientist at LinkedIn where he leads a team focused on building data products leveraging LinkedIn's powerful identity and reputation data.
The talk describes some techniques and best practices applied to develop products like LinkedIn Skills & Endorsements.
This talk was presented at the SF Data Science Meetup on September 19th, 2013
Great data leads to great insights which leads to great products.
Vitaly Gordon, senior products data scientist, talks about the culture, people and tools that have helped LinkedIn become the world’s leading professional social network and one of the most visited sites on the web.
Warren Buffet would often think of companies as castles with a competitive moat protecting the business. Products or companies that figure out how to build and leverage differentiated data assets will be best positioned to win their respective markets. This talk describes the properties of a good data moat, why it matters, and how to go about building them within your organization.
The Other 99% of a Data Science ProjectEugene Mandel
Slides from my talk at Open Data Science Conference 2016.
Algorithms and models are an important (and cool) part of data science. This talk is about all the other steps that it takes to deploy a data science project that makes a product slightly smarter. Stuff that you hear from practitioners, but is not covered well enough in books.
Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on November 20, 2013, at the "IBM Developer Days 2013" in Zurich, Switzerland.
ABSTRACT
There is no question that big data has hit the business, government and scientific sectors. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms big data and data science. This presentation gives a professional statistician's view on these terms and illustrates the connection between data science and statistics.
The presentation is also available at http://www.statoo.com/BigDataDataScience/.
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Booz Allen Hamilton created the Field Guide to Data Science to help organizations and missions understand how to make use of data as a resource. The Second Edition of the Field Guide, updated with new features and content, delivers our latest insights in a fast-changing field. http://bit.ly/1O78U42