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How I Learned to Stop Worrying and Love Linked Data
The document outlines a data science event held on August 9, 2017, featuring various talks and workshops focused on data science applications in finance, retail, and technology. Key speakers included experts from USAA, Domino Data Lab, and Capital One discussing topics such as the 'first mile problem' in data science, linked data, and the evolving role of analytics in the cloud. The event also addressed issues of bias, ethics, and social justice in the era of AI.
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How I Learned to Stop Worrying and Love Linked Data
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- 2. @datapopup A day offascinating talks and workshops on data science applications in finance, retail and technology
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- 4. Opening Remarks Nick Elprin Co-Founder& CEO, Domino Data Lab
- 5. Talk by Robert Welborn RobertWelborn AVP, Data Science, USAA @robert_welborn
- 6. Talk by Eduardo AriñoDe la Rubia Eduardo Ariño De la Rubia Chief Data Scientist, Domino @earino
- 7. Talk by Danny Monistere DannyMonistere SVP - Client Services, Nielsen @DannyMonistere
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- 9. The State of DataScience in Financial Services Fireside Chat Meltem Ballan Senior Consultant-Advanced Analytics, Clarity Insights Dave LaGassa Financial Services Divisional Data Officer, Capital One Fatih Akici Sr. Risk Analyst, ACE Cash Express Abhishek Goel @meltball @fatih_akici SVP Strategic Data Solutions, Citi Bank @IamAbhishekGoel @CapitalOne
- 10. Solving Data Science’sFirst Mile Problem: Or How I Learned to Stop Worrying and Love Linked Data Jon Loyens Chief Product Officer, Data World @jonloyens
- 11. About me and data.world Jon Loyens • Co-Founderand Chief Product Officer of data.world • VP of Engineering for Traveler Products at HomeAway • VP of Engineering and Director of the Labs group at Bazaarvoice. • A/B testing and data-driven product management is my bag • Openness, transparency and OSS is also my bag (and inspiration for data.world) • Kubrick fan • @jonloyens (pretty much everywhere)
- 12. for so manypeople, the first mile of data science is the last mile, because people just quit when they see the data. Prof. Eric Schwartz, University of Michigan “
- 13. DATA SCIENTISTS, SMES, ANALYSTS, RESEARCHERS The first mileproblem Finding, cleaning, transforming, merging data to prepare it for analysis 80% 1st Mile 2nd Mile 3rd Mile Analysis App building said building training datasets said cleaning/organizing data said Collecting datasets Data scientists’ least favorite aspect of their job: 10% 57% 21% All are first mile problems
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- 15. Slide title atthe top Knowledge?
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- 20. Great OSS is… • Well documented • Welcoming •Transparent • Has community • Standards
- 21. Great DS has… • Context • Documentation •Provenance • Community • Transparency • REPRODUCIBILITY
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- 23. The data analysisprocess -- deriving insights from the data -- is non-linear, investigative, and involves the data scientist manipulating the data in an analysis environment such as R or Python. With data.world's APIs and integrations, data scientists can seamlessly connect with data.world in order to directly load and publish data to the platform. INTEGRATED
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- 25. Linked data realizesthe vision of evolving the Web into a global data commons, allowing applications to operate on top of an unbounded set of data sources, via standardized access mechanisms. I expect that Linked Data will enable a significant evolutionary step in leading the web to it’s full potential. “ Tim Berners-Lee TED Talk 2009
- 26. Few formats conveyMEANING about the contents in a way that can be SHARED and EXTENDED.
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- 28. SAME WWW It’s applyingthe architecture as the of linked documents to… DATA
- 29. DATAFirst, break intoATOMIC FACTS
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- 33. Each dataset thatis added to the network INCREASES the incrementalVALUE of every data set in the network
- 34. So the PEOPLEand MACHINES who are using that data to solve HUMANITY'S BIGGEST PROBLEMS can leverage the sum of accumulated knowledge as effectively as possible.
- 35. Slide title atthe top
- 37. Leveraged Analytics atScale Paul Speaker Senior Data Scientist, The Dow Chemical Company @DowChemical
- 38. Building Serverless Data Pipelinesin the Cloud Manisha Sule Director of Big Data Analytics, Linux Acedemy @tweetDataS
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- 40. Talk by Sanjeev Kumar SanjeevKumar Head of Analytics & Data Science, Honeywell Oil & Gas @SanjeevK_
- 41. Talk by David Whiting DavidWhiting Head of Data Science Analysis, Capital One @CapitalOne
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- 43. Bias, Ethics, and SocialJustice in the age of AI Fireside Chat Eduardo Ariño De la Rubia Chief Data Scientist, Domino Sudha Subramanian Sr. Data Analyst, Sparkfish Anna Popova Data Scientist Senior Adviser, Dell @sravanankarajuSravan Ankaraju Founder, Data Scientist, Divergence Academy @gosparkfish @dell @earino @amdocsViswanath Puttagunta Sr Data Scientist, Amdocs
- 44. Closing Remarks Anna Anisin Creator,Data Pop-up @AnnaOnTheWeb
- 45. Thank you toour Sponsors: