Adi Wijaya - Scrum in Data Science, What Works and What Doesn’t
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The document discusses the relevance and effectiveness of the Scrum framework in data science practices, highlighting common challenges and a typical project timeline. It also elaborates on the roles of data scientists, data engineers, and other team members within the Scrum framework and provides an overview of the data science lifecycle and methodologies. Additionally, it emphasizes the integration of agile principles in data explorations to address business problems effectively.
Adi Wijaya - Scrum in Data Science, What Works and What Doesn’t
- 1. Data Science Scrum – What Works and Doesn’t Share Experience in Scrum on Data Science Practice, is it relevant or not?
- 2. Why Scrum & Data Science ? What Works & Doesn’t ? Data Science Demo T O P I C S
- 3. Adi Wijaya Co-Founder & Data Science Lead Poke me, and let’s talk about
- 4. Typical 2018 Data Science Requirement Proposal Business Understanding Data Preparation Create Model Evaluation Deployment 2 Weeks 1 Months 1 Months 1 Months 1 Months Common Data Science Project Plan That has High Risk to Fail
- 5. Typical 2018 Data Science Requirement Proposal Business Understanding Data Preparation Create Model Evaluation Deployment 2 Weeks 1 Months 1 Months 1 Months 1 Months Common Data Science Project Plan That has High Risk to Fail
- 6. Scrum Data Science Framework For complex adaptive problems Unified Stats, technology, Data Analysist, Business Knowledge To understand and analyze actual phenomena with data
- 7. Big Data Google Trends History of Data Science Popularity Today 2018 Timeline Big Data Google Data Worldwide 2014 - Now
- 8. Big Data Google Trends History of Data Science Popularity Today 2018 Timeline Hadoop Hadoop Big Data 2005 Google Data Worldwide 2014 - Now
- 9. Big Data Google Trends History of Data Science Popularity Today 2018 Timeline Hadoop Hadoop Big Data Data Science Data Science Google Data Worldwide 2014 - Now
- 10. Big Data Google Trends History of Data Science Popularity Today 2018 Timeline Hadoop Hadoop Big Data Data Science Data Science Software Development Scrum Software Development Scrum Google Data Worldwide 2014 - Now
- 11. SCRUM What Works and Doesn’t in Data Science Activity https://www.scrumguides.org/
- 12. 3 Pillars of Scrum Transparency Inspection Adaptive
- 13. 3 Pillars of Scrum Transparency Inspection Adaptive
- 14. Data Scientists What my Mom thinks I do What my Boss/Client think I do What I think I do What I Actually do
- 15. Scrum Framework Roles Rules Events Artifacts https://www.scrumguides.org/
- 16. “A Data Scientist is that a unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data” -- DJ Patil -- DJ Patil, former Linkedin and White House Data Scientist. Together with Jeff H (former Facebook) invent the term Data Scientist in 2011 What is Data Scientists?
- 17. “A Data Scientist is that a unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data” -- DJ Patil -- DJ Patil, former Linkedin and White House Data Scientist. Together with Jeff H (former Facebook) invent the term Data Scientist in 2011 What is Data Scientists?
- 18. Common 2018 Data Scientists = Machine Learning Engineers
- 19. Common 2018 Data Scientists = Machine Learning Engineers Adapt!
- 20. ROLESData Engineer Data Scientist Business Analysts Product Owner Scrum Master Business UnitBusiness Manager External Party Development Team Assist Presentation Assist Roles Scrum Data Science Team
- 21. ROLESData Engineer Data Scientist Business Analysts Product Owner Scrum Master Business UnitBusiness Manager External Party Development Team Assist Presentation Assist Roles Scrum Data Science Team
- 23. SCRUM EVENTS Kanban Board, Standup Meeting, Sprint Review
- 24. Kanban Board
- 25. Kanban Board
- 26. Stand Up Meeting
- 27. 1. Only Development Team! 2. Less than 15 Minutes! 3. Everyday
- 28. Sprint Review
- 29. 1. Involve Business 2. Less than 4 hours 3. Once in every 1-2 Weeks
- 30. Project Goals Data Business Problem Data Science Team Graph Analytics Text Analytics Path Analytics Machine Learning Define Business Problem Provide Data Science Team Insights, Recommendations and Workflow Doing Data Exploration Deliver Insights DataLabs AGILE ANALYTICS Service Contact for Engagement : adi@datalabs.id We help company to :
- 31. Week 1 Activity Timeline B Gath Wrangling Exploration Presentation& Evaluation Week 2 Week 3 Week 4Exploration Presentation & Evaluation Exploration Presentation & Evaluation Exploration Final Presentation Next Agile One Agile Phase As the spirit of true Big Data, we will explore your data according to the defined business use cases with adjustable priority on each week evaluation. We will deliver all results and findings we found when the agreed time is up. The result of one agile phase, can be continued for next agile phases. © Copyright 2018 DataLabs. All rights reserved. Not to be reproduced or shared without the prior written consent of DataLabs. Contact for Engagement : adi@datalabs.id
- 32. Data Science Cycle and Environment <= 2018
- 33. Data Engineer Data Scientist 1 Big Data Environment Create ETL job To extract sample data To csv Use FTP or even USB to transfer the data Jupyter Notebook Data Scientist 2 R Studio Jupyter Notebook to Data Engineer (Again sometimes using USB) • Rewrite notebook to scripts • Create API with other language • Deploy Data Science Team 2018 Life Cycle
- 34. Data Engineer Data Scientist 1 Big Data Environment Create ETL job To extract sample data To csv Use FTP or even USB to transfer the data Jupyter Notebook Data Scientist 2 R Studio Jupyter Notebook to Data Engineer (Again sometimes using USB) • Rewrite notebook to scripts • Create API with other language • Deploy Data Science Team 2018 Life Cycle
- 35. Data Engineer Big Data Environment Ideal Data Science Life Cycle Data Scientist 1 Data Scientist 2 Analytics Environment Maintain DataLake Maintain Production Model Optimize Performance Experimentation on Big Data Create Model Evaluate & Deploy ✓ One Environment ✓ Self Organizing ✓ Cross-functional
- 36. Data Engineer Cloudera Hadoop Ideal Data Science Life Cycle Data Scientist 1 Data Scientist 2 Maintain DataLake Maintain Production Model Optimize Performance Experimentation on Big Data Create Model Evaluate & Deploy ✓ One Environment ✓ Self Organizing ✓ Cross-functional Data Science Workbench
- 37. I Want to Predict Your Gender
- 38. T H A N K S F O R A T T E N D I N G