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Build a Career in Data Science

Emily Robinson
November 10, 2019

Build a Career in Data Science

“The best job in America.” “The sexist job of the 21st century.” Data Scientist, a title that didn’t even exist before 2008, is now the position employers can’t hire enough of and job seekers strive to become. With this popularity comes more and more people vying for entry-level data science jobs. How can you stand out from the crowd and actually land your first job as a data scientist?

In this talk, Emily will cover what skills you need to start your career, the different types of data scientist jobs, and how to best position yourself based on your academic and work history. She’ll show you how to make not only a standout resume and cover letter but also a strong data science portfolio of projects and blog posts. If you’ve been struggling to break into the field or are even just curious about what the data science hype is all about, this talk is for you.

Emily Robinson

November 10, 2019

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  1. Build a Career in Data Science Emily Robinson @robinson_es

  2. About Me ➔ Data Scientist for 3 years ➔ Background

    in statistics & social sciences ➔ Writing “Build a Career in Data Science” with Jacqueline Nolis
  3. Build a Career in Data Science 9 chapters out now,

    rest early 2020 40% off with code mtpdcds19 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.
  4. Build a Career in Data Science

  5. What is Data Science?

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

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

  8. Our (slightly updated) version

  9. Programming: What you need to know OR

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

  13. “Fake data scientists”

  14. “Must know” lists

  15. You don’t need to know everything

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

    Expand your network Find the right jobs Tailor your application
  17. Creating a Portfolio

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

    illustrates your data science skills
  19. How?

  20. Dataset -> Question

  21. Dataset -> Question

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

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

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

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

  26. 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
  27. The full process

  28. Put it on GitHub

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

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

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

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

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

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

  35. Expanding your Network

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

  37. Meetups – search on meetup.com

  38. Twitter

  39. Ask for help (use hashtags)

  40. Live tweet talks

  41. Share your work

  42. Share other people’s work

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

    • Suggests a specific time (that’s limited)
  44. Reaching out

  45. Finding the right job

  46. 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, Managing Director of Data Science at Teaching Trust, Chapter 5
  47. Figure out your specialty https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal/

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

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

  50. 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
  51. Tailoring your Application

  52. Writing a good resume • Include your GitHub and blog

    • Use clear, consistent formatting • Embrace whitespace • Limit to one page
  53. Quantify your impact • Ran A/B tests on email campaigns

    • Conducted 20 A/B tests on email campaigns, resulting in a 35% increase in click rate and 5% increase in attributed sales
  54. Relate to data science 1. Domain knowledge • Communication skills

    from teaching or consulting • Working in the domain (e.g. sales executive -> sales data scientist) 2. Mathematics & Statistics • Classes • Research 3. Programming & databases • Excel, Survey Monkey, Google Analytics, Tableau, SQL • Personal projects
  55. Writing a good cover letter • Try to find the

    hiring manager name • Tie together your experience • Not a repeat of your resume • Focus on what you offer the company • Tailor to the company
  56. Conclusion

  57. 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 • Tailoring your application
  58. Thank you! hookedondata.org @robinson_es datascicareer.com 40% off w/ mtpdcds19