Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Summary of Insights Learned from the Data Science Program Team Training


Published on

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.

Published in: Technology
  • Login to see the comments

Summary of Insights Learned from the Data Science Program Team Training

  1. 1. 1 Summary of Insights Learned from the Data Science Program Team Training Fred Chiang (@fredchiang) The Lead of Etu and the DSP Committee Member May 19th, 2014
  2. 2. 2 Agenda 1. What is DSP? 2. How did DSP become about? 3. What does DSP do? 4. What have we learned?
  3. 3. 3 What is DSP?
  4. 4. 4 Data Science Program (DSP) DSP was initiated by Etu, Code for Tomorrow (CfT) and supported by OKFN Taiwan and other various parties.
  5. 5. 5 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.
  6. 6. 6 How did it become about? What does it do? What have we learned?
  7. 7. 7 How did DSP become about?
  8. 8. 8 Harvard Business Review October 2012 But where can we find these sexy people and make them work with us?
  9. 9. 9 No one person can be the perfect data scientist, so we need teams Source: Next-Gen Data Scientist, Dr. Rachel Schutt Data Science Profiles
  10. 10. 10 Data Science Program Goal Train 300talented data science team members within 3years for Taiwan
  11. 11. 11 What does DSP do?
  12. 12. 12 DSP Working Group Committee CEO (CfT) / Principal Secretory (Etu) Administration Team COO (Etu) Curriculum Team CCO (CfT) Marketing Team CMO (CfT)
  13. 13. 13 DSP Courses (continuously developing) 1. Team Training 2. Data ETL and Analysis with Python 3. Data Journalism (coming soon)
  14. 14. 14 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 5 6 7 22 48 52 67 75 77% 23% Male Female Analyst Hygienist Campaigner Campaigner Designer Designer Designer
  15. 15. 15 Self-tagging by Role •  Campaigner •  Analyst •  Hygienist •  Designer
  16. 16. 16
  17. 17. 17 [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’
  18. 18. 18 [DSP’s Motto #2]
  19. 19. 19 •  2012.08 ~ 2013.09 •  All (22) counties/cities of Taiwan •  About 470,000 records Dataset 1: Real Estate Transaction Data
  20. 20. 20 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
  21. 21. 21 Data Fiesta: Team Project Showtime
  22. 22. 22 LOVE EASIER LIVING Infographic download: 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
  23. 23. 23 What have we learned?
  24. 24. 24 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.
  25. 25. 25 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
  26. 26. 26 Taipei, Taiwan Add : 318, Rueiguang Rd., Taipei 114, Taiwan Tel : +886-2-77201888 Fax : +886-2-87986069