Post on 09-Jan-2017
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
3 3/41 7/17, 2014 Hiroshi Maruyama
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
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
Goal: To Form A Network for Scalable Development of Talents
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Data Scientists
Certification
Industry Acade
mia
Share the Vision
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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“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
“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
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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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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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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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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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
Everyday Once a week
Once a month
2-3 times a week
Never
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
Q20. Why do you want to be a data analytics professional?
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020406080
100120140160180200
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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Finding 1: Datascientists have diverse background
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Business school
Mathematical Science
Commercial science
Hard science (e.g., physics, astronomy)
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
Finding 3: Data analytics is a capability of an organization, not of an individual
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VS
Datascientist
Data Analytics Team
Finding 4: Maturity of Acquirer's is also important
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Maturity of Acquirers is also important!
Statistics Center, President Toya
Difference between US and Japan
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Data Products Analytics Services
Individual Capability Organizational capability
1. Training Programs
– Online material
– Internship
2. Discussions on Career
– Crowd Soucing
3. Acquirer’s Maturity
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(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
(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
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”
“Co-Elevation” in Service Engagements
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Service provider and service receiver both learn from engagements
Kijima & Spohrer, 2010
• 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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