Since 2013 I have been working as Data Scientist – one of today’s hottest jobs in IT industry. During this time, I got the opportunities to experience the evolution of data science landscape — to see what worked and what didn’t. In this presentation, I will present some of my best learnings in the past 5 years, like foundations for building data science team, efficient ways for data scientists to work with other teams, skills that data scientists should have, and common fallacies in data science work

1. What I Learned About Data
Science in the Past 5 Years
Boxun Zhang

2. About me
• Currently leading data science at GoEuro
• 5 years at Spotify & a brief time at SoundCloud
• Ph.D. in Computer Science, on “How do people
use BitTorrent”

3. 1. Reproducibility is everything

4. –Karl Popper
“Non-reproducible single occurrences are of
no significance to science.”

5. non-reproducible work = lost knowledge

6. non-reproducible work = little value

7. How should reproducible work look like?

8. How should reproducible work look like?
Someone can understand the work and
produce the same results only using
documentation, report, code, and data,
except the help from the original author

9. Code for the maintainer

10. How to get there?

11. Code like engineers
How to get there?

12. Code like engineers
Version control data
How to get there?

13. Code like engineers
Version control data
Review insights
How to get there?

14. 2. Understand the basics well

15. One common challenge

16. One common challenge
How to learn data science stuff
effectively?

17. One common challenge
How to learn data science stuff
effectively?
machine learning

18. One common challenge
How to learn data science stuff
effectively?
machine learning
data mining

19. One common challenge
How to learn data science stuff
effectively?
machine learning
data mining
graph mining

20. One common challenge
How to learn data science stuff
effectively?
machine learning
data mining
graph mining
information retrieval

22. Statistics

23. Statistics Linear Algebra

24. Statistics Linear Algebra
Data structure &
Algorithm

25. What about cutting edge stuff?

26. Research papers

27. Research papers Student Internship

28. 3. Machine learning is the least
difficult part

29. XKCD: Machine Learning

30. Most models and algorithms are
commodity today

31. Training a model or developing an
algorithm is straightforward

32. Training a model or developing an
algorithm is straightforward
Once a problem is well defined

33. The hard parts

34. Identifying new problems from
business

35. Translating machine insights back to
business

36. Image from http://simplemindinc.com

37. Image from http://simplemindinc.com
complex models -> a few bullet points

38. 4. The best interview question

39. How to discover candidate’s true
potential

40. What
candidate
knows
What interviewers might ask

41. “Please introduce one of your
previous projects.”

42. “Please introduce one of your
previous projects.”
In other words, teach us something

43. Not specific

44. Not specific
Candidate is the expert

45. Not specific
Candidate is the expert
Easy to ask questions

46. Not specific
Candidate is the expert
Easy to ask questions
Always learn something

47. 5. The unwritten role

48. Data scientists often start with
predefined roles

49. Data scientists often start with
predefined roles
and work on given tasks from
stakeholders

50. The role of data scientist is still kinda
new and unique

51. The role of data scientist is still kinda
new and unique
Access to vast amount of data

52. The role of data scientist is still kinda
new and unique
Access to vast amount of data
Powerful tools for mining insights

53. Data scientists can also be explorers

54. Data scientists can also be explorers
To explore the unknowns

55. Thank you!
email: zhangboxun@gmail.com
twitter: @BoxunZhang
quora: Boxun Zhang