Build a Strong Data Science Portfolio

Transcript

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

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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
   

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3. Why have a portfolio?
   

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4. Build your skills
   

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

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6. Expand your Network
   

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7. Give Back
   

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

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9. Analyses
   

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10. How?
    

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11. Dataset -> Question
    

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12. Dataset -> Question
    

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

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

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

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

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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
    

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18. The Full process
    

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19. Put it on GitHub
    

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20. Blog posts
    

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21. Where?
    ➔ Easy & quick to set up
    ➔ Organic traffic (medium)
    ➔ Less customizability/control
    

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22. Where?
    ➔ Complete control
    ➔ Always free
    ➔ Little longer to set-up
    ➔ May get stuck debugging issues
    

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23. Explain your analysis
    https://theambitiouseconomist.com/an-analysis-of-the-gender-wage-gap-in-australia/
    

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

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

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

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27. Talks
    

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28. Not just for extroverts!
    

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29. Where to start?
    

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

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31. Snowball effect
    

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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
    

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33. Apps
    

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

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

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36. How?
    

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37. Share it!
    

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38. Open Source Projects
    

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

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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
    

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41. Is anything too small?
    https://jcahoon.netlify.com/post/2019/06/16/first-two-weeks-this-summer-at-rstudio/
    

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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
    

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43. Contribute to beginner-friendly issues
    bit.ly/drob-rstudio-2019
    

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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
    

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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!
    

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46. Conclusion
    

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

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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
    

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49. Thank you!
    bit.ly/erdatamatters
    hookedondata.org
    @robinson_es
    bit.ly/buildcareerds
    

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50. Benefits
    

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