How to Become a Data Scientist

How to Become a Data Scientist SF Data Science Meetup, June 30, 2014

  1. ryanorban
    How to Become a Data Scientist
    SF Data Science Meetup, June 30, 2014
    Transcript Header:
    How to Become a Data Scientist
    Transcript Body:
    • 1. Ryan Orban Co-Founder & CEO ryan@zipfianacademy.com @ryanorban
    • 2. Why are we talking about data science?
    • 3. Data Analyst Shortage Source: http://www.delphianalytics.net/wp-content/uploads/2013/04/GrowthOfDataVsDataAnalysts.png
    • 4. What is data science?
    • 5. Perfect Storm
    • 6. Technology Source: http://www.jcmit.com/diskprice.htm 0 1000 2000 3000 4000 1992 1997 2002 2007 2012 Capacity (GB) Cost per GB (USD)
    • 7. Unprecedented Data Growth
    • 8. Enter the Data Scientist
    • 9. What is Data Science? + Communication
    • 10. What do people look for in a data scientist?
    • 11. Broad-range generalist Deepexpertise T-Shaped Skillset
    • 12. T-Shaped Skillset Machine Learning, Statistics, Domain Knowledge Softw are EngineeringBusiness Acum en Distributed Com puting Com m unication
    • 13. Data Science Roles
    • 14. How to I become a data scientist?
    • 15. Data scientists need to know how to code.
    • 16. Python R Julia Java C++/GoScala/Clojure High-level Lower-level Learn to Code
    • 17. Learn to Code
    • 18. Data scientists need to be comfortable with mathematics & statistics.
    • 19. Mathematics Statistical Analysis Mathematics & Statistics Distributions (Binomial, Poisson, etc.) Summary Statistics (Mean, Variance, etc.) Hypothesis Testing Bayesian Analysis Linear Algebra (Matrix Factorization) Calculus (Integrals, Derivatives, etc) Graph Theory Probability/ Combinatorics
    • 20. Mathematics & Statistics
    • 21. Data scientists need know machine learning & software engineering.
    • 22. Distributed Computing Supervised (SVM, Random Forest) NLP / Information Retrieval Algorithms & Data Structures Data Visualization Data Munging Machine Learning & Software Engineering Machine Learning Software Engineering Validation, Model Comparison Unsupervised (K-means, LDA)
    • 23. Open-Source Data Science Masters
    • 24. SlideRule
    • 25. DataTau
    • 26. Learning data science can be really hard.
    • 27. ≠ Data Science
    • 28. Learning data science can be really hard.
    • 29. Context is King
    • 30. It’s about putting the pieces together
    • 31. Pathways: MS/PhD in Data Science Internship Immersive Programs Self-study
    • 32. You don’t need a PhD to do data science.
    • 33. Backgrounds Educational Background BS MS PhD 0 4 8 12 16
    • 34. Backgrounds Disciplines Software Engineering Analysts Finance/Economics Engineering Physics Physical Sciences Mathematics Statistics Astronomy Linguistics Professional Poker 0 2 4 6 8
    • 35. Backgrounds 94% Placement Rate91% Placement $115k avg. salary
    • 36. The Program • 12-week immersive bootcamp in San Francisco • Project-based curriculum with real datasets, solving actual problems • Guest lectures from leaders in the field • Personal mentorship to help students grow
    • 37. Timeline STRUCTURED CURRICULUM HIRING DAY CAPSTONE PROJECT GRADUATION 1 8 11 12 INTERVIEW PREP Program Timeline
    • 38. Learning Techniques
    • 39. Hiring Partners
    • 40. ! • Working knowledge of programming • Background in a quantitative discipline • Comfortable with mathematics and statistics • Child-like curiosity What We Look For
    • 41. Zipfian Academy Data Science Immersive Data Fellowship Data Engineering Immersive Weekend Workshops
    • 42. Zipfian Academy @ZipfianAcademy Data Science Immersive 12-weeks (Sep 8th) Weekend Workshops http://zipfianacademy.com/apply http://zipfianacademy.com/workshops Next: Interactive Visualizations w/ d3.js ( July 19 )
    • 43. The best way to learn data science is by doing data science.
    • 44. https://github.com/ipython/ipython/wiki/A-gallery-of- interesting-IPython-Notebooks
    • 45. Checklist: Learn the fundamentals Build out a project portfolio Apply! Blog about your experience
    • 46. A Practical Intro to Data Science http://bit.ly/learndatascience
    • 47. Thank You! Ryan Orban Co-Founder ryan@zipfianacademy.com @ryanorban
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Recent Reviews

  1. Kirubakaran Suruliraj
    Kirubakaran Suruliraj
    3/5,
    Version: 02/07/2014
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    Version: 02/07/2014
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  3. Jn Bjarki Gunnarsson
    Jn Bjarki Gunnarsson
    4/5,
    Version: 02/07/2014
    Thanks for this great presentation. Clear view and very interesting information on open source resources.
  4. Hatem Kotb
    Hatem Kotb
    5/5,
    Version: 02/07/2014
    Great presentation.
  5. KhaledMonsoor
    KhaledMonsoor
    3/5,
    Version: 02/07/2014
    a excellent piece of work ...
  6. Dan Audette
    Dan Audette
    5/5,
    Version: 02/07/2014
    Great slideshow on all of the aspects and paths of data science. I have been looking for a road map like this when communicating with our senior leadership. Thank you
  7. Pathya Budhiputra
    Pathya Budhiputra
    3/5,
    Version: 02/07/2014
    what a slide :) THANKS MANY THANKS
  8. SEOcial
    SEOcial
    5/5,
    Version: 02/07/2014
    Well put together—good stuff.
  9. Thomas Cai
    Thomas Cai
    4/5,
    Version: 02/07/2014
    This is great but on slide 18, since when is Java considered an Low-level language?
  10. sayedhamdani
    sayedhamdani
    3/5,
    Version: 02/07/2014
    How can I download the slide ?