Hiroshi Maruyama

1. Developing Data Analytics Skills in Japan:
Status and Challenge
Hiroshi Maruyama
The Institute of Statistical Mathematics
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International Workshop on Data Science and Service Research

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“Data Scientist: The Sexiest
Job of the 21st Century”

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http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation
McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity
Japan lags in producing data analytical talents

4. -5.3%
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Japan’s number is even declining …

5. MEXT started a project for developing talents for big data
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ISM + U. Tokyo awarded the grant for three year project
Budget: $130K x 3 years

6. Goal: To Form A Network for Scalable Development of
Talents
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Data
Scientists
Certific
ation
Industry
Acade
mia
Share the Vision

7. Five Work Streams of the Project
① Communication
② Rotation (internship)
③ Study on Best Practices
④ Develop Course Materials
⑤ Global Linkage
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So who are datascientists?

9. Mentor Companies
INSIGHT DATA SICENCE FELLOWS PROGRAM
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11. “Data Product” example: CouchTube
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“Datascientists” are those who develop working systems with data analytics
Scoring based on
data analytics
CouchTube.net

12. “Analyzing the Analyzers – An
Introspective Survey of Data Scientists
and Their Work”
by H. D. Harris, S. P. Murphy and M.
Vaisman
http://oreilly.com/data/stratareports/analyzing-the-analyzers.csp
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Survey in the US

13. O’reilly’s Survey
• Web forms (KwikSurveys.com)、5 pages, ave. 10 min. to fill
out
• Responders： 250
• Skills, experiences, education, self-image, web presence
スキルの選択項目（順列）
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14. Result of Clustering
Non-Negative Matrix Factorization法による
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15. Data Scientist Four Types
Binita
Data Businesspeople
• MBA
• Consulting
• Data analytics manager
at a large corporation
• Translator between data
and executives
Chao
Data Creatives
• Computer science major
• Startup company
experience
• Open source
development in spare
time
• Consider self as a hacker
Dmitri
Data Developer
• Computer Science major
• Professional programmer
Rebecca
Data Researcher
• Ph. D. in Science
• Originally in academia
• Good at writing academic
papers but no
management
experiences
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16. In Japan?
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17. Study on Current Status
• Quantitative： Survey on the applicants for
Statistical Skills Certification Test (319
respondents)
• Qualitative： Interviews with 20 “DataScientists”
– Industry ： Finance, manufacturing, distribution, public
sector, IT vendor, consulting firms, …
– Size： From freelancers to large
– Roles： Analytics in line business, internal consulting,
external consulting,
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18. Survey contents
• Q1-Q3: Demography
• Q4-6： Industry, roles
• Q7-10: Data analysis works (frequency,
purposes, etc.)
• Q11-18: Skills – IT/Statistics/Business – and
how they learned them
• Q19-20: Career path
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19. Demography
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Total 319, 11% female

20. Q7. Frequency of data analysis
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全くない 月1日 週1日 週2・3日 毎日
0
10
20
30
40
50
60
70
80
90
EverydayOnce a
week
Once a
month
2-3 times
a week
Never

21. On Careers
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A. 全くそう思わない
B. 少しはそう思う
C. どちらともいえない
D. そう思う
E. かなりそう思う
Q18. Do you think your skills are
effectively utilized?
Q19. Do you want to have a
career as a data analytics
professional?
Strongly disagree
Slightly disagree
Slightly agree
Strongly agree
Neutral

22. Q20. Why do you want to be a data analytics professional?
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0
20
40
60
80
100
120
140
160
180
200

23. Our clustering result …
Established engineer in
a large manufacturing
company. Does data
analytics as a part of
line business (e.g.,
mechanical design,
quality assurance, …)
Young, eager to be a
datascientist, but has
little experiences
Professional consultant
with long experiences
in data analytics. Proud
of being a data analyst.
Female in a SMB
company, doing
market analysis.
Datascientist is an
appealing career
because of work
flexibility.
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24. Finding 1： Datascientists have diverse
background
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Business school
Mathematical Science
Commercial science
Hard science (e.g., physics, astronomy)

25. Finding 2： Data Scientists are “whole mind” skills
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Business Issues
Business Decisions
① Find
② Solve
③ Apply
Mathematical Formulation
Numeric Solution
Analyst / modeler
True
“Datascientist”
ISBN-13: 978-4062882187

26. Finding 3： Data analytics is a capability of
an organization, not of an individual
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VS
Datascientist
Data Analytics Team

27. Finding 4： Maturity of Acquirer's is also
important
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Maturity of Acquirers
is also important!
Statistics Center, President Toya

28. Difference between US and Japan
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Data Products Analytics Services
Individual Capability
Organizational capability

29. So What’s Next?
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30. 1. Training Programs
– Online material
– Internship
2. Discussions on Career
– Crowd Soucing
3. Acquirer’s Maturity
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31. (1) Training: Online Material
“Data Scientist Crash Course”
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Contents （20min. × ８）
0. Overview
1. What is Data Scientist
2. Data Analysis 101
3. Visualization and Tools
4. Statistical Modeling and Machine Learning
5. Modeling Time-Series Data
6. Optimization
7. Data Analytics and Decision Making
8. Intellectual Property in Data Analytics

32. (1) Training: Internship Program
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33. (2) Career: Is Freelance Data Scientist a Viable Option?
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Experiment:
Post a data analysis
task on a crowd
sourcing site
Igawa, et al., “An Exploratory Study of Data Scientists in Crowd Sourcing,” The
16th Convention of Japan Tele-Work Society, 2014.
10 Workers

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Key: How to Distinguish Best Workers?
Best Workers
Worst Workers
Contracted Workers

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Best Workers
Worst Workers
Contracted Workers

36. Skill Certification Program is being Developed
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http://www.datascientist.or.jp/

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Analytics Skills
Service
Providing Skills
Service
Receiving Skills
(3) Services: Skills for “Data Analytics as Service”

38. “Co-Elevation” in Service Engagements
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Service provider and service receiver both learn from
engagements
Kijima & Spohrer, 2010

39. • Are there skills / techniques / best practices
for service providers that facilitate co-
elevation during service engagements?
– E.g. Some consultants are reluctant to disclose all
their knowledge to the client because they fear
losing next contracts
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40. Thank You
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maruyama@acm.org
Twitter: @maruyama