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Build a Strong Data Science Portfolio

Emily Robinson
June 24, 2019

Build a Strong Data Science Portfolio

Emily Robinson

June 24, 2019


  1. Emily Robinson Data Scientist at DataCamp Build a Strong Data

    Science Portfolio
  2. About Me ➔ Background in the social sciences ➔ R

    programmer ➔ Formerly at Etsy ➔ Data Scientist at DataCamp ➔ Writing a book on data science careers with Jacqueline Nolis
  3. Why have a portfolio?

  4. Build your skills

  5. Advance your Career http://varianceexplained.org/r/year_data_scientist/

  6. Expand your Network

  7. Give Back

  8. Some components Analyses Blog posts Talks Apps Open Source Projects

  9. Analyses

  10. How?

  11. Dataset -> Question

  12. Dataset -> Question

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

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

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

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

  17. 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
  18. The Full process

  19. Put it on GitHub

  20. Blog posts

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

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

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

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

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

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

  27. Talks

  28. Not just for extroverts!

  29. Where to start?

  30. First talk Jared Lander (meetup organizer) asked me to speak

  31. Snowball effect

  32. CFPs (Call for Proposals) ➔ Look for conferences offering first-

    time speaker help ➔ Be succinct ➔ Focus on the outcome for attendees ➔ Make it topical ➔ R-Ladies abstract review
  33. Apps

  34. App for following conference tweets https://gadenbuie.shinyapps.io/tweet-conf-dash/

  35. Tweet mashup https://tweetmashup.com/

  36. How?

  37. Share it!

  38. Open Source Projects

  39. Help yourself & others https://www.rstudio.com/resources/videos/contributing-to-tidyverse-packages/

  40. Documentation is a great place to start There are many

    ways to contribute to scikit-learn … Improving the documentation is no less important than improving the library itself. - From scikit-learn contributing guide (emphasis mine) https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md
  41. Is anything too small? https://jcahoon.netlify.com/post/2019/06/16/first-two-weeks-this-summer-at-rstudio/

  42. The process https://thisisnic.github.io/2018/11/28/ten-steps-to-becoming-a-tidyverse-contributor/ ➔ Watch the repos ➔ Ask if

    you can help w/ issues ➔ Make the changes & submit a pull request
  43. Contribute to beginner-friendly issues bit.ly/drob-rstudio-2019

  44. If you’ve copied and pasted code three times, write a

    function If you’ve used the same function across three analyses, write a package Making your own package/library
  45. Isn’t making a package for “advanced programmers”? From Susan Johnston,

    as used in Jim Hester’s RStudio 2018 conference talk https://resources.rstudio.com/rstudio-conf-2018/you-can-make-a-package-in-20-minutes-jim-hester qCan you open and run R / Python? qCan you install a package? qCan you write code? qCan you write a function? qCan you learn to write a function? Excellent, you can write a package!
  46. Conclusion

  47. Potential Components Analyses Blog posts Talks Apps Open Source Projects

  48. Additional resources ➔ Making Peace with Personal Branding by Rachel

    Thomas ➔ Overcoming Social Anxiety by Steph Locke ➔ Keeping up with blogdown by Mara Averick ➔ Advice to Aspiring Data Scientists: Start a Blog by David Robinson ➔ Speaking at conference by Cassie Kozyrkov ➔ List of speaking resources ➔ R packages book by Hadley Wickham and Jenny Bryan
  49. Thank you! bit.ly/erdatamatters hookedondata.org @robinson_es bit.ly/buildcareerds

  50. Benefits