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Building an Applied Science Portfolio
- 1. OPEN DATA SCIENCE CONFERENCE San Francisco | Oct. 28 - Nov. 1, 2019
- 2. Building an Applied Science Portfolio 2 Ben Weber Data Science @ Zynga October 31, 2019
- 4. 4 Applied Science at Zynga Goal: Build Portfolio-Scale Data Products Example Projects • Propensity Models • Recommendation Systems • Player Segmentation • Anomaly Detection Tech Stack: AWS, Python, Databricks, CouchBase
- 5. 5 Why Build a Portfolio? Showcase your experience Practice writing Learn new tools Practice deployments Avoid take-home projects
- 6. 6 • Get hands-on with the cloud • Create a new data set • Glue things together • Stand up a service • Create a stunning visualization • Write a white paper Advice for Aspiring Data Scientists
- 7. 7 • Get hands-on with the cloud • Create a new data set • Glue things together • Stand up a service • Create a stunning visualization • Write a white paper Advice for Aspiring Applied Scientists Themes • Ecosystems • Scale • Integrations • Authoring
- 8. Ecosystems 8
- 9. 9 Programming Ecosystems Interview Question • Tell me about a time that you put a model into production. Follow-up Questions • How did you make updates to the model? • What team was responsible for model failures? • Did you consider different deployment approaches?
- 10. 10 Programming Ecosystems Goals • Get hands-on with different cloud environments • Learn tradeoffs between different platforms and tools Projects to explore: • Setting up a predictive model as an endpoint • Deploying a model as a serverless function • Building a reproducible environment • Persisting models to cloud storage
- 11. 11 Getting Started with Cloud Tools • AWS Free Tier • GCP Credits • Databricks community edition
- 13. Models as Serverless Functions 13
- 14. Containers for Reproducible Models 14
- 15. Scale 15
- 16. 16 Computing at Scale Interview Question • What is the biggest data set that you’ve worked with? Follow-up Questions • How did you distribute the problem? • How did subtasks coordinate? • How would you work with live data? • How scalable is your approach?
- 17. 17 Computing at Scale Goal • Use datasets so large that you have to use distributed methods Approaches • Spark • Cloud Dataflow • Calculations in SQL
- 18. Public Datasets Open Data on AWS BigQuery Public Datasets Kaggle Government Data (SEC.gov) 18
- 19. 19 Bootstrap Calculations in Redshift
- 20. 20 Recommendation Engine in Vertica
- 21. PySpark for Distributed Computing 21
- 22. Cloud Dataflow for Batch Models 22
- 23. Integrations 23
- 24. 24 Integration Projects Interview Question • Tell me about a time where you had to integrate different systems. Follow-up Questions • Why was this approach necessary? • Are there other implementations that you considered? • How would you approach this problem now?
- 25. 25 Integration Projects Goal • Build end-to-end model pipelines Approaches • Use portable model formats • Leverage new language features • Use managed services • Write a wrapper
- 27. 27 Fully-Managed Game Analytics Pipeline A streaming pipeline for sending game data to GCS and BigQuery
- 28. 28 Tetris Simulations with Cloud Dataflow
- 30. Authoring 30
- 31. 31 Writing about Projects Interview Question • How do you like to advocate for your projects? Follow-up Questions • What is your medium of choice? • Have your projects been extended by others?
- 32. 32 Writing about Projects Go beyond decks! Long-Form Content • White papers • Blog posts • Book chapters
- 33. 33 Writing Topics Learning Something New “Deploying Keras Deep Learning Models with Flask” Deep Dive “Why & How Words With Friends Is Adopting React Native” Project Postmortem “Words With Friends 2 & How We Built Lightning Round”
- 34. 34 White Papers Detailed Project Overviews - Executive summary - Project Overview - Details - Appendix Use a platform with comments
- 36. Blogging 36
- 38. Books 38
- 39. Wrapping Up 39
- 40. 40 Portfolio Themes Ecosystems: get hands-on with cloud computing Scale: explore distributed computing Integrations: glue things together Authoring: go beyond decks
- 41. 41 Building an Applied Science Portfolio Ben Weber Distinguished Data Scientist Zynga bweber@zynga.com @bgweber https://www.zynga.com/jobs/