Building Data Teams The document discusses building effective data teams. It notes that the shortage of data scientists is expected to reach 190,000 by 2018 due to the fact that 80% of new data scientist jobs are currently unfilled. It emphasizes that data teams require diverse skills, including statistics, engineering, business analysis, and communication. It also outlines principles for effective "street fighting" data science, such as focusing on results, robust evaluation, and checking assumptions. Soft skills are highlighted as important. The document concludes by discussing increasing data maturity and the growth of data science and big data as fields.

1. Building Data Teams
Lessons Learned & What’s Next

2. Big Data - Definition

3. Big Data - Definition
George Dyson, Strata conference in London
2012:
“the era of big data started when the human
cost of making the decision of throwing
something away became higher than the
machine cost of continuing to store it.”

4. Data Science - Definition

5. Teaching 1 - No Unicorns

6. ● 80% of new data scientists jobs are not filled
● By 2018 the US will have a shortage of
190.000 data scientists
Teaching 1 - No Unicorns
Source: http://blogs.wsj.com/cio/2014/02/14/it-takes-teams-to-solve-the-data-scientist-shortage/ &
http://blog.pivotal.io/pivotal/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage

7. Teaching 1 - No Unicorns
Finding Data Scientist Unicorns…
Source: http://www.forbes.com/sites/danwoods/2012/03/08/hilary-mason-what-is-a-data-scientist/ &
http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

8. Teaching 1 - No Unicorns
Source: http://www.oreilly.com/data/free/files/analyzing-the-analyzers.pdf

9. Teaching 1 - No Unicorns
Hacking &
Engineering
Statistics &
Analytics
DATA
TEAM
Business Analysis
& Communication

10. Teaching 2 - Street Fighting DS
*Source: http://de.slideshare.net/pskomoroch/street-fighting-data-science-12072010

11. Teaching 2 - Street Fighting DS
*Source: http://de.slideshare.net/pskomoroch/street-fighting-data-science-12072010
● Focus on results, improvise
● Careful with complex models
(Occam’s Razor)
● Check your assumptions
● Look at your data, literally
● Robust evaluation processes

12. Teaching 3 - No Tech Wars

13. Teaching 4 - Soft Skills
... matter!
Source: http://data-informed.com/soft-skills-matter-data-science/

14. What’s next?

15. What’s next? - Data Maturity
Data Maturity
Competitive
Advantage
Collect
Data
DWH,
Ad Hoc
Reports
Standard
Reports,
Analytics
AB-Testing,
Growth
Hacking
Predictive Modelling,
Multivariate Analysis
Recsys,Data Products
Data Democratization, Business Science

16. What’s next?
Data Science Big Data
Source: http://www.gartner.com/newsroom/id/2819918,
Gartner Hype Cycle for Emerging Technologies 2014

17. Source: http://www.google.com/trends/explore#q=%22data%20science%
22&cmpt=date
What’s next?

18. Summary
● No unicorns
● Street fighting data science
● No tech wars
● Soft skills matter

19. Contact
twitter: @jmoldvay
email: janos@jimdo.com
blog: blabladata.com