Data Scientist Toolbox
Upcoming SlideShare
Loading in...5

Like this? Share it with your network


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!



Total Views
Views on SlideShare
Embed Views



7 Embeds 79 26 21 12 9 8 2 1



Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

Data Scientist Toolbox Presentation Transcript

  • 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