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Data Science Demystified
- 2. What is Data Science?
- 4. What is data science?
- 5. What do data scientists do?
- 6. What is Data Science? The reality
- 7. https://hackernoon.com/the-ai-hierarchy-of-needs-18f111fcc007 AI needs a lot of support
- 8. What is a data scientists?
- 9. What do data scientists do? The Reality
- 10. What do data scientists do? The Reality
- 11. One definition https://hackernoon.com/what-on-earth-is-data-science-eb1237d8cb37, Cassie Kozyrkov Data science is the discipline of making data useful
- 12. Classic data science venn diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
- 13. Our (slightly updated) version
- 14. Programming OR
- 15. Benefit of programming Accessibility Web APIs SQL Databases Historical Data httr DBI dbply r Slide from https://www.youtube.com/watch?v=KreEV7p6dnU by Chris Cardillo
- 16. Code away repetitive tasks Code around obstacles Limit Human Error Benefit of programming Efficiency Slide from https://www.youtube.com/watch?v=KreEV7p6dnU by Chris Cardillo
- 17. Benefit of programming Collaboration Increased Shareability Communicable Processes Dependable Replicability Slide from https://www.youtube.com/watch?v=KreEV7p6dnU by Chris Cardillo
- 18. Code away repetitive tasks Code around obstacles Limit Human Error Benefits of programming Accessibility Efficiency Collaboration Web APIs SQL Databases Historical Data Increased Shareability Communicable Processes Dependable Replicability httr DBI dbply r Slide from https://www.youtube.com/watch?v=KreEV7p6dnU by Chris Cardillo
- 19. Mathematics & statistics 1. What techniques exists • I need to group customers together -> I should try clustering 2. How to apply them • How to do a k-means clustering in R/Python 3. How to choose which to try • What clustering method will work best?
- 20. Statistics: going beyond the numbers Which is greater? 4 out of 10 390 out of 1000
- 21. Statistics: going beyond the numbers Who are the worst batters? http://varianceexplained.org/r/credible_intervals_baseball/ Who are the best batters?
- 22. Statistics: going beyond the numbers http://varianceexplained.org/r/credible_intervals_baseball/
- 23. How can we split our customers into different groups to market to? How can we run a clustering algorithm to segment customer data? Business question Data science question A k-means clustering found 3 distinct groups Data science answer Business answer Here are 3 types of customers: new, high spending, commercial Domain knowledge - Renee Teate, @BecomingDataSci Skills: • Communication • Empathy • Understanding your data (where it lives, built-in assumptions, edge cases)
- 25. Analytics Pulled from Airbnb Careers • Define and evaluate key metrics • Develop dashboards • Communicate analyses • Comfortable in SQL • Industry experience
- 26. Algorithms From Airbnb Careers • Deep Learning techniques • Natural language processing • Strong programming skills • Developing ML models at scale in
- 27. Inference From Airbnb Careers • Run strategic analysis • Design experiments • Improve statistical methodology • PhD in quantitative field
- 28. Three completely different job descriptions From Airbnb Careers • Deep Learning techniques • Natural language processing • Strong programming skills • Developing ML models at scale in • Define and evaluate key metrics • Develop dashboards • Communicate analyses • Comfortable in SQL • Industry experience • Run strategic analysis • Design experiments • Improve statistical methodology • PhD in quantitative field
- 29. How Do I Grow my Data Science Skills?
- 30. Become a data scientist in 3 easy steps https://towardsdatascience.com/from-data-analyst-to-data-scientist-f67a724ea265, Ben Stanbury
- 32. You don’t need to know everything
- 33. #1 Advice Practice by making data science personal projects
- 34. Why? “Don’t get stressed about keeping up with the cutting edge of the field … You should start by getting very comfortable transforming and visualizing data, programming with a wide variety of packages, and using statistical techniques like hypothesis tests, classification, and regression.” - David Robinson, Data Insights Engineering Manager at Flatiron Health, Chapter 4
- 35. How?
- 39. Tip 1: Include visualizations https://hackernoon.com/more-than-a-million-pro-repeal-net-neutrality-comments-were-likely-faked-e9f0e3ed36a6
- 40. Tip 2: choose a topic you’re excited about https://masalmon.eu/2018/01/01/sortinghat/
- 41. Tip 3: Limit your scope https://kkulma.github.io/2017-08-13-friendships-among-top-r-twitterers/
- 42. Making progress Inspired by bit.ly/drob-rstudio-2019 Less valuable More valuable Idea Getting data Cleaning Exploratory Final resultModeling Less valuable More valuable Work only on your computer Work online (GitHub, Blog, Kaggle) How I used to think about analyses How I think about analyses now
- 43. The full process
- 44. Put it on GitHub
- 45. Conclusion
- 46. The potential future of data scientists From https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists “It wouldn’t surprise me if the [data scientist] title goes the way of the ‘webmaster’” - Hilary Mason
- 47. Resources • Day in the life of a data scientist webinar by David Robinson • You’re not paid to model by Jacqueline Nolis • Doing data science at Twitter by Robert Chang • Succeeding as a data scientist in small companies/startups by Randy Au • How to change careers and become a data scientist – one quant’s experience by Rachel Thomas • What data scientists really do, according to 35 data scientists by Hugo Bowne- Anderson
- 48. Getting started with data science
- 49. Thank you! hookedondata.org @robinson_es datascicareer.com 40% off w/ pcrobinson
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