Build a Career in Data Science

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

1. Build a Career in Data Science Emily Robinson @robinson_es
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.
3. Build a Career in Data Science
4. What is Data Science?
5. One definition https://hackernoon.com/what-on-earth-is-data-science-eb1237d8cb37, Cassie Kozyrkov Data science is the discipline of making data useful
6. Classic data science venn diagram http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
7. Our (slightly updated) version
8. Programming: What you need to know OR
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?
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)
11. How Do I Become a Data Scientist?
12. “Fake data scientists”
13. “Fake data scientists”
14. “Must know” lists
15. “Must know” lists
16. You don’t need to know everything
17. How to find a data science job Create a portfolio Expand your network Find the right jobs
18. Creating a Portfolio
19. What is a portfolio? A public body of work that illustrates your data science skills
20. How?
21. Dataset -> Question
22. Dataset -> Question
23. Question -> Dataset http://varianceexplained.org/r/trump-tweets/
24. Tip 1: Include visualizations https://hackernoon.com/more-than-a-million-pro-repeal-net-neutrality-comments-were-likely-faked-e9f0e3ed36a6
25. Tip 2: choose a topic you’re excited about https://masalmon.eu/2018/01/01/sortinghat/
26. Tip 3: Limit your scope https://kkulma.github.io/2017-08-13-friendships-among-top-r-twitterers/
27. 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
28. The full process
29. Put it on GitHub
30. Where? ➔ Easy & quick to set up ➔ Organic traffic (medium) ➔ Less customizability/control
31. Where? ➔ Complete control ➔ Always free ➔ Little longer to set-up ➔ May get stuck debugging issues
32. Explain your analysis https://theambitiouseconomist.com/an-analysis-of-the-gender-wage-gap-in-australia/
33. Teach a concept https://juliasilge.com/blog/stack-overflow-pca/
34. Share your experience https://d4tagirl.com/2018/08/rstudio-conf-diversity-scholarships-for-the-win
35. Give advice www.rladiesnyc.org/post/2019-nyr-conference-tips/ towardsdatascience.com/prioritizing-data-science-work-936b3765fd45
36. Expanding your Network
37. How people get data science jobs http://www.rctatman.com/files/Tatman_2018_DataSciencePortfolios_DC.pdf
38. Meetups – search on meetup.com
39. Twitter
40. Ask for help (use hashtags)
41. Live tweet talks
42. Share your work
43. Share other people’s work
44. 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
45. Reaching out Check out https://www.datahelpers.org/ by Angela Bassa
46. Reaching out
47. Finding the right job
48. 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
49. Figure out your specialty https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal/
50. You don’t need to meet all of the “requirements”
51. 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
52. Conclusion
53. 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
54. Thank you! hookedondata.org @robinson_es datascicareer.com 40% off w/ code ctwdsgo20