The document describes how to build a data science team and systems. It discusses establishing data collection and management systems, developing metrics and dashboards to analyze business data, creating predictive models using machine learning algorithms, and providing data science services like information retrieval to internal customers. The goal is to move from static, uncollected data to a fully realized big data platform and data science team that supports business analytics and decision making.

1. How to Build
a Data Science Team
and Systems
이름: 강지훈
소속: 피키캐스트 데이터사이언스실

2. How Important is
Data Science?
2

3. 3
Data Scientist Marketer Business Planner Developer
Trends
Source: Google Trends

4. 4
Trends

5. 5
Trends | Pikicast
3M MAU
12M DAU
Contents Curation Media
“세상을 즐겁게”

6. 6
피키캐스트 배달의 민족 카카오택시
Trends | Pikicast
Source: Google Trends

7. 7
Before Data Science Team
Previous Data Management

8. 8
•

13. Tapjoy

14. (5Rocks)

15.
• .

16.
• Uncollected

17. HTTP

18. Access

19. Logs
Previous Data Management

20. 9
What We Do
Data Science Team RR

21. 10
• Business Data Analytics
• Advanced Predictive Analytics
• Algorithms
• Big Data Engineering
Data Science Team RR

22. 11
• Log Scheme
Big Data Engineering
• Collection
• Big Data Platform

23. 12
Log Scheme
Fast Decision in Startups
BUT…

24. 13
Log Scheme
Static vs Dynamic
with Compression Issue

25. 14
Hybrid
TSV + JSON
Log Scheme: Solution

26. 15
• Log Scheme
Big Data Engineering
• Collection
• Big Data Platform

27. 16
vs ?
Collection

28. 17
Collection

29. 18
• Log Scheme
Big Data Engineering
• Collection
• Big Data System and Platform

30. 19
Big Data System and Platform

31. 20
Big Data System and Platform

32. 21
• Business Data Analytics
• Advanced Predictive Analytics
• Algorithms
• Big Data Engineering
Data Science Team RR

33. 22
• Metrics Management
Business Data Analytics
• Dashboard
• Visualization Reports

34. 23
• DAU, MAU
• Cohort, Retention, Returning Visitor
• Contents Metrics
• User Behavior
• Marketing ROI
Metrics Management
• New Metric Development

35. 24
New Metric Development
기대공간대비 효율
Efficiency by Location
• a metric for content performance
• modified version of Click-Through Rate (CTR)
specialized for Mobile Feed Environment
• embedded as features in smart timeline system
Metrics Management

36. 25
• Metric Management
Business Data Analytics
• DashBoard
• Visualization Reports

37. 26
DashBoard
• Systems
(request, consulting, communications)
• Internal Customer
• External
• Reporting
• Data Visualization

38. 27
DashBoard
Confidential

39. 28
DashBoard
• Overall service performance
Confidential

40. 29
DashBoard
• Content performance
Confidential

41. 30
• Editor performance
DashBoard
Confidential

42. 31
• Metric Management
Business Data Analytics
• Dashboard
• Visualization Reports

43. 32
with
• Tools
Visualization Reports

44. 33
Visualization Reports
• Examples

45. 34
• Examples
Visualization Reports

46. 35
• Examples
Visualization Reports

47. 36
• Business Data Analytics
• Advanced Predictive Analytics
• Algorithms
• Big Data Engineering
Data Science Team RR

48. 37
• Cases
Advanced Predictive Analytics
Smart Timeline,
Personalization Recommendation

49. 38
On Time
With Quality
Matching
Contents
Score /
Rearrange
Cases
Contents
Contents
Contents
Contents
Smart Timeline | Overview

50. 39
• Time Sensitive
• Informational
• Attractive
• Engagement
• Matching(CF, topic, etc.)
Cases
Smart Timeline | Feature extraction

51. 40
Cases
Smart Timeline | Model

52. 41
Cases
Personalization Recommendation
topic model
shallow/deep
learning
Current
Smarter

53. and

54.
More

55. Precise
New Version

56. 42
Cases
Deep(Shallow) Learning
Hierarchical Topic Model
Undirected Graphical Model

57. 43
• Business Data Analytics
• Advanced Predictive Analytics
• Algorithms
• Big Data Engineering
Data Science Team RR

58. 44
• Information Retrieval
Algorithms
Search Engine

59. 45
Information Retrieval
Open Source?
Elastic Search?

60. 46
Information Retrieval
Open Source vs. Own Code

61. 47
Information Retrieval
Open Source
(Elastic Search)
Own code
Pros
Customizable UI
Easy Monitoring
Easy Management
Theory-Based
Fast Way
Cons

62. Output
Complete Library
API
48

63. 49
A data scientist
has to make a complete software
to internal customers(developers).

64. Research

66. Group

67. Study
Team

68. Bonding

69. and

70. Beyond
50

71. Future Works
Copyright Classification Model
Image Processing Analysis
51

72. QA

73. Thank You