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.

Transcript

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

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2. About Me
   ➔ Data Scientist for 3 years
   ➔ Background in statistics & social sciences
   ➔ Writing “Build a Career in Data Science”
   with Jacqueline Nolis
   

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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.
   

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4. Build a Career in Data Science
   

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5. What is Data Science?
   

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

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

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8. Our (slightly updated) version
   

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9. Programming: What you need to know
   OR
   

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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?
    

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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)
    

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12. How Do I Become a Data Scientist?
    

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13. “Fake data scientists”
    

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14. “Must know” lists
    

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15. You don’t need to know everything
    

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16. How to find a data science job
    Create a portfolio
    Expand your network
    Find the right jobs
    Tailor your application
    

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17. Creating a Portfolio
    

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18. What is a portfolio?
    A public body of work
    that illustrates your
    data science skills
    

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19. How?
    

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20. Dataset -> Question
    

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21. Dataset -> Question
    

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

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

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

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

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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
    

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27. The full process
    

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28. Put it on GitHub
    

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

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

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

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

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

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

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35. Expanding your Network
    

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

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37. Meetups – search on meetup.com
    

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38. Twitter
    

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39. Ask for help (use hashtags)
    

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40. Live tweet talks
    

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41. Share your work
    

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42. Share other people’s work
    

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43. Reaching out
    • Mentions their work
    • Offers a topic
    • Suggests a specific time (that’s
    limited)
    

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44. Reaching out
    

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45. Finding the right job
    

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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
    

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47. Figure out your specialty
    https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal/
    

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

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

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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
    

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51. Tailoring your Application
    

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52. Writing a good resume
    • Include your GitHub and blog
    • Use clear, consistent formatting
    • Embrace whitespace
    • Limit to one page
    

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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
    

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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
    

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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
    

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56. Conclusion
    

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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
    

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58. Thank you!
    hookedondata.org
    @robinson_es
    datascicareer.com
    40% off w/ mtpdcds19
    

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