Summary of Insights Learned from the Data Science Program Team Training
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1 Summary of Insights Learned from the Data Science Program Team Training Fred Chiang (@fredchiang) [email protected] The Lead of Etu and the DSP Committee Member May 19 th , 2014
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Who really has the skills and talents to leverage the most value out of data? The Data Science Program (DSP) was co-founded by Code for Tomorrow and Etu. We believe that building and deploying a data science team consisting of members who possess and have the ability to utilize their different skill sets from a variety of industries is more practical and realistic. Versus hoping to find an individual data scientist who is an expert in a wide variety of technical fields ranging from math, statistics, and visualizations, as well as a solid background in other fields such as business, communication, and etc. The Data Science Program has identified four pertinent categories to place our members into. These four categories are Campaigner, Data Analyst, Data Hygienist, and Designer. Each team will have these four categories filled. During the training every team learns how to do data processing, data analysis, and visualization together with the sole purpose to use these skills to solve a common problem. After four weeks of intensive study, each team comes up with enterprise-grade team projects demonstrating the innovation of data-driven businesses. After two rounds of DSP Team Training, DSP has accumulated 10 team projects and has graduated more than 60 alumni who are passionate about data science. During this journey of developing and deploying teams trained in data science, the most valuable aspects we walked away with was the witnessing of members growing in confidence from the learning and experience, the building of team work, and the overall growth of each individual. At the end of the day, our hope of as members of DSP, including myself is to instill and motivate more people to devote themselves to the exploration of data science. Now think about how you can do the same.
Transcript of Summary of Insights Learned from the Data Science Program Team Training
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Summary of Insights Learned from the Data Science Program Team Training �
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Fred Chiang (@fredchiang)
The Lead of Etu and the DSP Committee Member May 19th, 2014
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Agenda
1. What is DSP? 2. How did DSP become about? 3. What does DSP do? 4. What have we learned?
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What is DSP?
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Data Science Program (DSP) DSP was initiated by Etu, Code for Tomorrow (CfT) and supported by OKFN Taiwan and other various parties.
http://datasci.co
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DSP, is a case of enterprise run data-driven CSR with NPO, from SYSTEX/Etu’s perspective
Etu, SYSTEX Etu is a pioneer of Big Data providing Hadoop-based solutions from Taiwan primarily focused on helping customers discover, unlock, and utilize valuable information embedded in extremely large data sets through simple steps. SYSTEX Group is an Asia-Pacific regional IT service provider and the largest one in Taiwan.
Etu is an independent brand incubated by SYSTEX.
Code for Tomorrow Code for Tomorrow (CfT) Foundation Initiative is a non-profit organization that actively encourages governments, private sectors, and civil society organizations to embrace the power of the internet and people to better our governance in the 21st century.
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codefortomorrow.org
How did it become about? What does it do?
What have we learned?
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How did DSP become about?
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Harvard Business Review October 2012 http://cromi.org/main/wp-content/uploads/2012/10/Davenport-2012-data-scientist.pdf
But where can we find these sexy people and make them work with us?
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No one person can be the perfect data scientist, so we need teams�
Source: Next-Gen Data Scientist, Dr. Rachel Schutt�
Data Science Profiles�
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Data Science Program Goal
Train 300 talented data science team members
within 3 years for Taiwan�
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What does DSP do?
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DSP Working Group
Committee�
CEO (CfT) / Principal Secretory (Etu)�
Administration Team�
COO (Etu)�
Curriculum Team�
CCO (CfT)�
Marketing Team�
CMO (CfT)�
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DSP Courses (continuously developing)�
1. Team Training 2. Data ETL and Analysis with Python 3. Data Journalism (coming soon)
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Who are interested? Those who signed up for DSP Team Training #1 & #2. Totaling 168 counts
0 10 20 30 40 50 60 70 80
UI Designer
Art Designer
UX Designer
Other
Product/Service Planner
Story Teller
Programmer
Data Analyst
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6"
7"
22"
48"
52"
67"
75"
77%"
23%"
Male Female
Analyst
Hygienist
Campaigner
Campaigner
Designer
Designer
Designer
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Self-tagging by Role�• Campaigner • Analyst • Hygienist • Designer
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[DSP’s Motto #1]
“The point of statistics is not to do myriad rigorous mathematical calculations; the point is to gain insight into meaningful social phenomena.”
~ Charles Wheelan
from the book ‘Naked Statistics: Stripping the Dread from the Data’
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[DSP’s Motto #2]
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• 2012.08 ~ 2013.09 • All (22) counties/cities of Taiwan • About 470,000 records
Dataset 1: Real Estate Transaction Data �
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Dataset 2: PIXNET’s open data�
The largest blog service provider in Taiwan Data opened: 1. Metadata of popular photo 2. Photo EXIF�3. Metadata of popular blog 4. Visitor logs of popular blog
*Article and photo can be retrieved by API
www.pixnet.net
http://developer.pixnet.pro/
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Data Fiesta: Team Project Showtime�
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LOVE & EASIER LIVING Infographic download: http://goo.gl/fKdXXi
Elder’s Happiness Index by a number of medical treatment resources, disease & death, education resources, recreation resources, and social participation of every district in Taipei
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What have we learned?
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Insights Learned from DSP Team Training�1. Potential Data Science Members are everywhere.
But this does not matter without the ability to organize them and to train them to reach their potential.
2. Access to individual specialized classes are available. But there are a lack of classes that combine all this knowledge and integrate it to become a complete End-to-End course.
3. There is a great amount of Data out there, especially within the Government. But the Government lacks a powerful strategic plan of how to open data for the betterment of society.
4. Insights are around us. But these insights need to be turned into actions.
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More or Less�
1. More Quality in Life, Less Cynic 2. More Real Strategy, Less Bluffing 3. Ｍore Data, Less Guessing 4. More Correlation, Less Summation 5. More Cross-over, Less Limitation
Do them right,
let Data Science help to make many things good
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Taipei, Taiwan Add : 318, Rueiguang Rd., Taipei 114, Taiwan Tel : +886-2-77201888 Fax : +886-2-87986069 www.etusolution.com�
