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

-5.3%

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Japan’s number is even declining …

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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So who are datascientists?

Mentor Companies

INSIGHT DATA SICENCE FELLOWS PROGRAM

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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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Result of Clustering Non-Negative Matrix Factorization法による

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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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In Japan?

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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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Demography

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Total 319, 11% female

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

So What’s Next?

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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

(1) Training: Internship Program

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(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

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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Thank You

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maruyama@acm.org Twitter: @maruyama