In this presentation, Jon Loyens will share: -Best practices for sharing context and knowledge about your data projects -How linked data can augment your existing data science workflow and toolchain to accelerate your work -How a social network can unlock power of Linked Data and data collaboration -How Linked Data can help you easily combine private and Open Data for fun and profit

1. DAL
AUG 9, 2017
#datapopup

2. @datapopup
A day of fascinating talks and workshops
on data science applications in finance,
retail and technology

3. Breakfast + Registration

4. Opening Remarks
Nick Elprin
Co-Founder & CEO, Domino Data Lab

5. Talk by
Robert Welborn
Robert Welborn
AVP, Data Science, USAA
@robert_welborn

6. Talk by Eduardo
Ariño De la Rubia
Eduardo Ariño De la Rubia
Chief Data Scientist, Domino
@earino

7. Talk by
Danny Monistere
Danny Monistere
SVP - Client Services, Nielsen
@DannyMonistere

8. Lunch and Networking

9. The State of
Data Science 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’s First 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-Founder and 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 many people, 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 mile problem
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

14. 18,000,000
open datasets

15. Slide title at the top
Knowledge?

16. How we think about data today…

17. How we communicate about data today…

20. Great OSS is…
• Well documented
• Welcoming
• Transparent
• Has community
• Standards

21. Great DS has…
• Context
• Documentation
• Provenance
• Community
• Transparency
• REPRODUCIBILITY

22. Need a way of capturing continuous context
Integrated Social Linked

23. The data analysis process -- 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

24. SOCIAL – BRING PEOPLE &
DATA TOGETHER

25. Linked data realizes the 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 convey MEANING
about the contents in a way that can be
SHARED and EXTENDED.

27. Data
Information
Knowledge
Wisdom

28. SAME WWW It’s applying the architecture as the
of linked documents to… DATA

29. DATAFirst, break into ATOMIC FACTS

30. SUBJECT, PREDICATE, OBJECT
( )

31. UNIVERSAL IDENTIFIERS
is about publishing data as
LINKED DATA
ATOMIC FACTS
and using
to refer to concepts and relationships, so we
can agree upon the SEMANTIC MEANING
of data.

32. Data can enjoy a
“NETWORK EFFECT”

33. Each dataset that is added to the network
INCREASES the incrementalVALUE
of every data set in the network

34. So the PEOPLE and 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 at the top

37. Leveraged Analytics at Scale
Paul Speaker
Senior Data Scientist,
The Dow Chemical Company
@DowChemical

38. Building Serverless Data
Pipelines in the Cloud
Manisha Sule
Director of Big Data Analytics, Linux Acedemy
@tweetDataS

39. Coffee Break

40. Talk by
Sanjeev Kumar
Sanjeev Kumar
Head of Analytics & Data Science,
Honeywell Oil & Gas
@SanjeevK_

41. Talk by
David Whiting
David Whiting
Head of Data Science Analysis,
Capital One
@CapitalOne

42. Coffee Break

43. Bias, Ethics, and
Social Justice 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 to our Sponsors: