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Build a Career in Data Science

Build a Career in Data Science

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

Emily Robinson

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

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  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.

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  3. Build a Career in Data Science

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  4. What is Data Science?

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

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  7. Our (slightly updated) version

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  8. Programming: What you need to know
    OR

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  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?

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  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)

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  11. How Do I Become a Data Scientist?

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  12. “Fake data scientists”

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  13. “Fake data scientists”

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  14. “Must know” lists

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  15. “Must know” lists

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  16. You don’t need to know everything

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

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  18. Creating a Portfolio

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

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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. Ask for help (use hashtags)

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  39. Live tweet talks

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  40. Share your work

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  41. Share other people’s work

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  42. 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

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  43. Reaching out
    Check out
    https://www.datahelpers.org/
    by Angela Bassa

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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, Community Advocate at Kaggle, 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. 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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  50. 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

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  51. Thank you!
    hookedondata.org
    @robinson_es
    datascicareer.com
    40% off w/ code
    ctwdsgo20

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