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Data Scientist Toolbox


My presentation at on how to get your job done as data scientist!

My presentation at on how to get your job done as data scientist!

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  • 1. Data Scientist Toolbox Andrei Savu - 2013
  • 2. Me• Founder of• Organizer of Bucharest JUG (• Passion for DevOps, Data Analysis• Connect with me on LinkedIn
  • 3. @ Axemblr• Service Deployment Orchestration• Infrastructure Automation (DevOps)• Apache Hadoop On-Demand Appliance• Axemblr Provisionr
  • 4. (Big)Data in a nutshell• Business Intelligence / Research Evolved• Significant change in Decision Making• Enables new Products & Features• Enables new Business Models
  • 5. Data Scientist• Has a Business / Research oriented perspective• Knowledge of statistics & software engineering (AI, infrastructure)• Ability to explore questions and formulate hypotheses to be tested
  • 6. Data Science Project• Focused on particular business goals• Based on a set of important questions• Result > Answers that support business decisions
  • 7. The Algorithm• Find *Important* • Create Pipelines Questions • Automate & Deploy• Identify & Extract Data • Learn & Repeat!• Store & Sample• Analyse• Visualization
  • 8. Start w/ “Big” Questions ... answer them with (Big)DataHow can we understand & improve the conversion rate? How can we increase customer satisfaction? How can we find important mentions in social media?
  • 9. Identify Data Sources OR add more probes / sensors as needed Google Analytics,Web server logs, Mixpanel, Customapplication metrics, Mouse tracking, Facebook metrics etc.
  • 10. Extract Data... to a medium that allows you to run arbitrary queriesLocal filesystem, Databases, Hadoop, HBase, HDFS, Hive, Pig
  • 11. Extract• Database dump tool, replicas or backups• External web services• Apache Sqoop (SQL-to-Hadoop)• Implement pipelines / real-time streams• Write custom tools as needed
  • 12. CurateUnfortunately Data is Messy
  • 13. Curate - Your Way• Use or develop tools / scripts• On large volumes there no obvious choices• Custom ways of filtering & aggregating large streams (e.g. twitter, sensors)• Reuse existing software components for data curation / validation
  • 14. DataWrangler Interactive System for Data cleaning a transformation
  • 15. Open Refine Former Google Refine OpenRefine
  • 16. Sample (time, etc.)As needed to support interactive exploration
  • 17. Why Sample?• Interactive exploration to create and check assumptions, to create algorithms• Be careful with “Statistical Significance”• Sample Smart: By time, By location etc.
  • 18. Analyse Sample This is were the fun begins
  • 19. Analyse Sample• Create models• Create algorithms• Check hypotheses• Faster feedback loops & Immediate Gratification
  • 20. Excel-like
  • 21. Python
  • 22. RStudio
  • 23.
  • 24. Analyse Allapply your results to the entire data set
  • 25. How to Analyse All?• “Easy” on a single machine• Go distributed w/ Hadoop, MPI, Storm, Oracle Exa* etc.• Key: Leverage existing tools• Tools: sed, awkSQL, RHadoop, Apache Hive, Pig, Cloudera Impala, MPI, Custom MR
  • 26. VisualizationCommunicate meaning w/ Graphics
  • 27.
  • 28. Automate & Deploy Make it part of your internal dashboard
  • 29. Learn & RepeatAnswer most of the time generate new questions
  • 30. Thanks! Questions? Andrei Savu / @andreisavu