Build a Career in Data Science

Build a Career in Data Science

Advice for starting a career in data science, presented at DataScienceGO Virtual.

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Emily Robinson

June 20, 2020
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  1. Build a Career in Data Science Emily Robinson @robinson_es

  2. Build a Career in Data Science 40% off with code

    ctwdsgo20 at datascicareer.com (code is good for 40% off everything on Manning) Build a Career in Data Science teaches you what school leaves out, from how to land you first job, to the lifecycle of a data science project, and even how to become a manager.
  3. Build a Career in Data Science

  4. What is Data Science?

  5. One definition https://hackernoon.com/what-on-earth-is-data-science-eb1237d8cb37, Cassie Kozyrkov Data science is the discipline

    of making data useful
  6. Classic data science venn diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

  7. Our (slightly updated) version

  8. Programming: What you need to know OR

  9. 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?
  10. 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)
  11. How Do I Become a Data Scientist?

  12. “Fake data scientists”

  13. “Fake data scientists”

  14. “Must know” lists

  15. “Must know” lists

  16. You don’t need to know everything

  17. How to find a data science job Create a portfolio

    Expand your network Find the right jobs
  18. Creating a Portfolio

  19. What is a portfolio? A public body of work that

    illustrates your data science skills
  20. How?

  21. Dataset -> Question

  22. Dataset -> Question

  23. Question -> Dataset http://varianceexplained.org/r/trump-tweets/

  24. Tip 1: Include visualizations https://hackernoon.com/more-than-a-million-pro-repeal-net-neutrality-comments-were-likely-faked-e9f0e3ed36a6

  25. Tip 2: choose a topic you’re excited about https://masalmon.eu/2018/01/01/sortinghat/

  26. Tip 3: Limit your scope https://kkulma.github.io/2017-08-13-friendships-among-top-r-twitterers/

  27. Making progress Inspired by bit.ly/drob-rstudio-2019 Less valuable More valuable Idea

    Getting data Cleaning Exploratory Final result Modeling 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
  28. The full process

  29. Put it on GitHub

  30. Where? ➔ Easy & quick to set up ➔ Organic

    traffic (medium) ➔ Less customizability/control
  31. Where? ➔ Complete control ➔ Always free ➔ Little longer

    to set-up ➔ May get stuck debugging issues
  32. Explain your analysis https://theambitiouseconomist.com/an-analysis-of-the-gender-wage-gap-in-australia/

  33. Teach a concept https://juliasilge.com/blog/stack-overflow-pca/

  34. Share your experience https://d4tagirl.com/2018/08/rstudio-conf-diversity-scholarships-for-the-win

  35. Give advice www.rladiesnyc.org/post/2019-nyr-conference-tips/ towardsdatascience.com/prioritizing-data-science-work-936b3765fd45

  36. Expanding your Network

  37. How people get data science jobs http://www.rctatman.com/files/Tatman_2018_DataSciencePortfolios_DC.pdf

  38. Meetups – search on meetup.com

  39. Twitter

  40. Ask for help (use hashtags)

  41. Live tweet talks

  42. Share your work

  43. Share other people’s work

  44. Reaching out • Mentions their work • Offers a topic

    • Suggests a specific time (that’s limited) https://medium.com/@treycausey/do-you-have-time-for-a-quick-chat-c3f7e46de89d
  45. Reaching out Check out https://www.datahelpers.org/ by Angela Bassa

  46. Reaching out

  47. Finding the right job

  48. Let go of the “data scientist” title “Think about how

    attached you are to the data scientist title. If you decide to not concern yourself with what you’re called and to instead focus on the work that you’re doing, you’ll have a lot more flexibility to find jobs.” - Jesse Mostipak, Community Advocate at Kaggle, Chapter 5
  49. Figure out your specialty https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal/

  50. You don’t need to meet all of the “requirements”

  51. Consider the type of company matters Criteria Massive tech Retailer

    Startup Mid tech Government Contractor Bureaucracy Freedom Salary Job security Chances to learn Chapter 2, How data science works at different companies
  52. Conclusion

  53. Take-away points • You don’t need to know everything •

    There’s no such thing as a “fake data scientist” • Let go of the “data scientist” title • Focus on: • Creating a portfolio • Expanding your network • Finding the right job
  54. Thank you! hookedondata.org @robinson_es datascicareer.com 40% off w/ code ctwdsgo20