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Enterprise Data Science
- 1. Data Science in the Enterprise Misha Lisovich misha@honestbuildings.com
- 2. Outline 1. Data Science in the enterprise 2. Interviews!
- 3. Enterprise Data Science ● What does a data scientist do? ● How do you make the most impact?
- 4. The Process 1. Productize - Compelling data products - Innovation pipeline 2. Ruggedize - Toolchain: Rstudio, Devtools, Github, Travis CI, Docker - Strong testing - Production-ready Architecture 3. Assimilate - Command line tools - Make it into HTTP APIs - Make it into Docker containers
- 5. Step 1: Productize Internal Products: - Ad-hoc Analyses - Internal Dashboards - Automated reports - Rapid Prototyping External Products: - End-user data products - Backend services
- 6. 1. Dashboards Business Intelligence Internal ToolsData & Job Monitoring
- 7. 2. Automated Reports .Rmd -> html =
- 9. 4. Backend Services Batch Data Processing (ETL) R APIs
- 11. Step 2: Ruggedize 1. Create reproducible architecture 2. Set up strong testing & CI 3. Separate Production and Dev 4. Set up monitoring & reporting
- 12. Case Study: HB Architecture - Rstudio - Containerized Architecture - Continuous Integration - Multiple Environments - Notifications/Monitoring
- 13. Data Architecture elasticsearch: image: elasticsearch shiny-server: image: shiny ports: - "443:443" links: - elasticsearch etl: image:etl volumes: - .:/data etl-data: image: etl-data ETL Shiny Server Elastic ETL Data SQL S3 Web rAPI SQL Shiny Server Elastic ETL data ETL rAPI Docker ComposeContainers + = Rstudio Server
- 15. Continuous Integration Github Travis CI commit latest-stable tag Production pull latest-stable Staging pull latest-stable Success!
- 16. Docker Registry/Rolling Back Docker Registry ETL data volume Changes Deployed to Prod Save Versioned Image Danger! Need to Rollback! ETL data volume Load Older Image Docker Registry
- 17. Step 3: Assimilate! (i.e., be kind to your devs)
- 18. Assimilate (contd) - HTTP APIs - OpenCPU, rapier - Docker containers - Rocker - Command line tools - Rscript, littler, docopt
- 19. Interviewing ● What we want ○ Problem solving ability ○ Typical question: how would you approach a problem we are currently working on? ● What others want ○ It depends! :) ○ Orgs with DS will ask you the standard DS questions
- 20. Interviewing Orgs without DS will ask you about 1. Search / recommendations 2. SQL 3. Data engineering (how would you use Hadoop to …?) 4. Software development Orgs without DS will evaluate you as a 1. Subject matter expert (search/recs) 2. Software engineer 3. DBA / SQL analyst 4. Product manager Be prepared! (study ‘Cracking the Interview’ or similar)