The document discusses the role and responsibilities of a data scientist. It describes how data scientists take large amounts of messy data and use skills in math, statistics, and programming to organize and analyze the data to uncover solutions to business problems. An effective data scientist has strong skills in both statistics and software engineering. The document also outlines the scientific process that data scientists follow, including developing algorithms and models, testing hypotheses on data, deploying solutions, and continuously monitoring and improving based on results.

1. Life of Data Scientist
myths and reality
Jeong, Buhwan Ph.D
Data Hacker / Kakao Corp.

2. Data scientists
are big data wranglers. They take an enormous
mass of messy data points and use their formidable
skills in math, statistics and programming to clean,
massage and organize them. Then they apply all
their analytic powers and domain knowledge to
uncover hidden solutions to business challenges.
Script (modified) from http://www.mastersindatascience.org/careers/data-scientist/

3. A data scientist is someone who is
better at statistics than any software engineer and
better at software engineering than any statistician.
domain knowledge
business understanding+
http://www.mastersindatascience.org/careers/data-scientist/

4. Diagram from https://www.quora.com/What-is-a-data-scientist-3

5. Image from http://paper4pc.com/superman-logosuperman.html

6. http://www.sintetia.com/wp-content/uploads/2014/05/Data-Scientist-What-I-really-do.png

10. DB
Log
SQL
Data
TXT / EXL
Visualization
Implement
Test & Deploy
[KR]
Algorithm
- Regression
- Classification
- Clustering
[Insight]

11. Big Data?

12. Volume Variety
Velocity Value

13. Value
BIG & FAST SMART
Count & Trend Predictive
Technical Meaningful
Analytics
Volume Variety
Velocity
Engineering Science

14. Data Science
Scientific Method
Proved by
Theory
Verified with
Experiment
Algorithm
(Equations)
Testing
(Evidence)

15. Experiment & Test

16. Hypothesis
Experiment
Graduation

17. Observation
Deployment
Test (Comparison)

18. Observation
Off-line Test
Deployment
On-line Test

19. Test Deploy
Modeling Test set
Observe
A (Treatment)
B (Control)
← Offline : Online →
Solve

20. M T W T F S S
Code Release
Off-line Test
On-line Test
Deployment
Monitoring & Improvement
Netflix’s Weekly Test & Deployment

21. Image from https://vwo.com/ab-testing/
On-line A/B Test

22. Image from https://vwo.com/ab-testing/

23. From Yahoo! (Creative Best Practices: Native Ads)

24. From Yahoo! (Creative Best Practices: Native Ads)

26. A/B Test Configuration
Traffic-driven
For every incoming request,
if random() < 0.1, then assign the treatment group (10%)
otherwise, assign the control group (90%)
User-driven
For every requestor (whose userId ends with ‘NN’)
if ‘NN’ is in ’00 ~ 09’, then assign the treatment group
otherwise, assign the control group

27. Random
Control Group
Treatment (A)
A/B Test
Random
Control Group
Treatment A
Treatment B
Treatment C
Multivariate Test

28. Multivariate test: https://www.optimizely.com/resources/multivariate-testing/

32. Red Daum vs Blue Daum

33. Data over Algorithm

34. Forbes.com: http://goo.gl/bauDHw

35. DB
Log
SQL Data
Implement
Test & Deploy
[KR]
Algorithm
- Regression
- Classification
- Clustering
[Insight]
20 60
15
5

36. Forbes.com: http://goo.gl/bauDHw

37. Hacking Data for business goals
- Right data
- Right algorithm
- Right evaluation

38. Good UI/UX
is defined by
User Adoption

39. Human Hacker
Image from https://goo.gl/vClux5

40. Enjoy your Jeju