This document discusses startups, lean startup methodology, big data, and data science. It defines startups and describes lean startup culture. Big data is defined as the exponential growth of data in our lives due to technology advances. The 4 V's of big data are described. Data science involves statistical analysis, data mining, and predictive modeling to answer different types of questions. A/B testing is discussed as a way to test hypotheses and improve products. The document provides an overview of the data science field and skills required.

1. Startup
@teguhn

3. Bukalapak Mission: Empowering SMEs in Indonesia

4. What is startup?
“a business or undertaking that has recently begun operation”
The American Heritage Dictionary
“a company designed to grow fast”
Paul Graham
“a temporary organization used to search for a repeatable
and scalable business model”
Stave Blank

5. Startup Culture
● Creative problem solving
● Open communication
● A flat hierarchy

9. Lean Startup

10. Lean Approach
http://www.slideshare.net/Leananalytics

11. Lean Approach
http://www.slideshare.net/Leananalytics

12. Big Data
@teguhn

13. Big Data is cultural phenomenon
It describes how much data
is part of our lives,
precipitated by accelerated
advances in technology
http://www.internetlivestats.com/

14. 4 V’s

15. Value Chains of Big Data
● Infrastructure
(Data Origins to Data Integration)
● Analytics
(Data Science world)
● Applications
(Implementation)

16. Big Data Landscape

17. Data Science
@teguhn

18. Why Data Science?

19. LinkedIn Hottest Skills
“(statistical analysis and data mining) It is the only
skill category that is consistently ranked in the top 4
across all of the countries we analyzed.”
http://blog.linkedin.com/2016/01/12/the-25-skills-that-can-get-you-hired-in-2016/

21. Skills of Data Scientists
http://www.business2community.com/big-data/big-data-and-analytics-value-chain-cross-section-0589031

22. Skills of Data
Scientists

24. Types of questions
● Descriptive: What happened?
● Exploratory: See patterns in variables
● Inferential: Estimating variables population
● Causal: Does x affect y?
● Predictive: Predict values for another object
Image : http://www.gs.washington.edu/academics/courses/akey/56008/lecture/lecture1.pdf

25. Test your hypothesis with A/B Testing

26. Why A/B Testing?
● Randomization experiment
● Confirming which idea is better
● Only some users will be tested
● Development effectivity
● Know the significance of the change
● Utilizing traffic
Image : http://www.gs.washington.edu/academics/courses/akey/56008/lecture/lecture1.pdf

27. teguh@bukalapak.com
● Data Scientist
● Lead of Growth Team

28. References
https://rjmetrics.com/resources/reports/the-state-of-data-science/
https://datasciencespecialization.github.io
http://www.gs.washington.edu/academics/courses/akey/56008/lecture.htm
http://datascopeanalytics.com/blog/six-qualities-of-a-great-data-scientist/
http://conferences.oreilly.com/strata/big-data-conference-uk-2015/public/schedule/detail/39814
https://datajobs.com/what-is-data-science
http://mattturck.com/2016/02/01/big-data-landscape/
http://www.business2community.com/big-data/big-data-and-analytics-value-chain-cross-section-0589031
Lillian Pierson, “Data Science for Dummies”