Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

No Downloads

Total views

2,049

On SlideShare

0

From Embeds

0

Number of Embeds

10

Shares

0

Downloads

32

Comments

0

Likes

1

No embeds

No notes for slide

- 1. WINNING<br />WITH<br />BIG <br />DATA<br />Secrets of the Successful<br />Data Scientist<br />SDForum BI SIG<br />June 15, 2010<br />Michael Driscoll<br />@dataspora<br />
- 2. WHY DATA<br />MATTERS<br />NOW<br />
- 3. THE INDUSTRIAL<br />AGE <br />OF <br />DATA<br />
- 4. WHAT IS <br />BIG DATA?<br />Data that is distributed.<br />
- 5. WHAT IS<br />DATA <br />SCIENCE?<br />
- 6. WHY DATA SCIENCE<br />IS SEXY<br />
- 7. “The sexy job in the next ten years will be statisticians…”<br />- Hal Varian<br />=<br />+<br />
- 8.
- 9. data<br />model<br />1000 bytes<br />2 bytes<br />
- 10. 9 WAYS TO WIN<br />WITH DATA<br />
- 11. 1. CHOOSE THE<br />RIGHT TOOL<br />You don’t need a chainsaw to cut butter.<br />
- 12. 2. COMPRESS EVERYTHING<br />mysqldump -u myuser -p mypasssourceDB | <br />gzip | ssh mike@dataspora.com "cat - | <br />gunzip | mysql -u myuser -p mypasstargetDB"<br />The world is IO-bound.<br />
- 13. 3. SPLIT UP<br />YOUR DATA<br />Split, apply, combine.<br />
- 14. 4. WORK <br />WITH SAMPLES<br />perl -ne "print if (rand() < 0.01)" <br /> data.csv > sample.csv<br />Big Data is heavy, <br />samples are light.<br />
- 15. 5. USE<br />STATISTICS<br />
- 16. COPY<br />FROM OTHERS<br />git clone git://github.com/kevinweil/hadoop-lzo<br />Use open source.<br />
- 17. 7. ESCHEW CHART TYPOLOGIES<br />Charts are compositions,<br />not containers.<br />
- 18. 8. COLORWITH CARE<br />Color can enhance <br />or insult.<br />
- 19. 9. TELL A STORY<br />People are listening.<br />
- 20. ONE <br />SUCCESS<br />STORY<br />
- 21. WHY DO TELCO CUSTOMERS LEAVE?<br />Sign up<br />Leave<br />Goal: “less churn.”<br />
- 22. DATA:<br />BILLIONS<br />OF CALLS<br />… and millions of callers.<br />
- 23. DOES CALL <br />QUALITY<br />MATTER?<br />… a difference,<br />but not significant.<br />
- 24. WHAT ABOUT<br />SOCIAL<br />NETWORKS?<br />Hmmm...<br />
- 25. BUILD THE <br />CALL GRAPH<br />… but is it predictive?<br />
- 26. EVOLUTION OF A CALL GRAPH<br />April<br />
- 27. EVOLUTION OF A CALL GRAPH<br />May<br />
- 28. EVOLUTION OF A CALL GRAPH<br />June<br />
- 29. EVOLUTION OF A CALL GRAPH<br />July<br />
- 30. 700% INCREASE<br />IN CHURN<br />when a cancellation<br />occurs in a call network.<br />
- 31. FINAL <br />THOUGHTS<br />
- 32. THE BIG DATA STACK<br />Actions<br />Data Products<br />(Content Filters, Rec Engines)<br />Analytics<br />(R, SPSS, SAS, SAP)<br />Insights<br />Big Data<br />Dedicated RDBMS <br />Data<br />
- 33. THANKS!<br />QUESTIONS?<br />Michael Driscoll<br />med@dataspora.com<br />@dataspora on Twitter<br />http://www.dataspora.com/blog<br />SDForum BI SIG<br />June 15, 2010<br />

No public clipboards found for this slide

Be the first to comment