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Educational Backgrounds of Data Industry Professionals

As some of you may know, I co-founded a group in the Bay Area for women who work in the data industry with Chloe Tseng back in March. These past 8 months have been extremely rewarding for me. Not only have I been sharpening my organizing and community building skills but I've built an amazing network of friendship and support. 

One thing that continually will come up in our conversations is the idea of "non-traditional" vs. "traditional" educational backgrounds. "Traditional" referring to professionals who have a STEM (i.e. Computer Science, Statistics, Math, etc.) background versus those of us who have a degree in a liberal arts field (i.e. Communications, Business, PoliSci, etc.). It's really interesting to see how this manifests in the types of struggles they face. Speaking personally, I studied Political Science and had a few non-data jobs before entering this space. I've always felt a bit behind from peers who have STEM backgrounds which causes me stress and a lot of after hours self-learning activities to "keep up". I've connected with many others who feel similarly. However, we have had amazing guests and attendees at our events with masters or doctorates in Statistics and other STEM fields. They are also very driven and do take self-improvement just as seriously. 

It's very difficult to find data on this divide* - especially specific to people who work in the data industry. I've looked at statistics from the Department of Education and know that in the mid-1980s, 37% of computer science majors were women whereas in 2013-14 only 18%. But the reality is that there are many people in our space who don't have a Computer Science degree, or have a different STEM degree, and they're very successful in their craft.

In an effort to learn more about data industry professionals and their backgrounds, I've created a short 8 question survey that I would appreciate if you take and share on your social networks and with your colleagues. I am hoping to get a good sample size and plan to use some of this information in a conference presentation.


* I did find a survey about new coders. It's a great data set too for analysis!


  1. Hi Brit! I love your twitter! I'm currently in school studying communications, and eventually I think I'd like to go into data science. Would you say it's easier to get into programming and then go into data science, or is it possible to just dive right into Data? my boyfriend works at Slalom, he actually met you at that meeting last week, and mentioned you have a meetup for women in data, so I'll definitely be checking that out! :)

    1. Here is my take, which you can take or leave :) I definitely believe anyone can get into the field of data science - education and background are important, but not necessities for entering the industry. I went to school to be a Teacher, and am now a Trainer for Tableau (and I love working for the company!) Data Science can change lives, particularly when combined with a love for a tool like Tsbleau!

    2. Hi Dalia,

      First - apologies for such a long delay in my response! Thanks so much for taking the time to comment. In my opinion, without having an advanced degree inline with the expectation of being a data scientist (whether it's an actual degree in Data Science or a related/helpful discipline such as Computer Science, Statistics, or Mathematics) there's other paths that might be more helpful to start with if your ultimate goal is data science - especially in the Bay Area. As an example of a career journey to data science:

      1. Business Analyst within a Business Intelligence Team - will help to understand business processes, requirements design, interviewing techniques, working cross-functionally, etc.

      2. Data Analyst (Beginner) - Start working with SQL to query data and answer business questions. Perhaps more of your world would be with relational databases or Excel. Begin learning a tool like Tableau to visualize data. Practice presenting findings.

      3. Data Analyst (Intermediate to Advanced) - Begin working with unstructured data and/or big data. Start answering more impactful business questions. Get more involved with testing. Start bringing together data from different sources. You likely will be doing more data cleanup. Start learning other data analysis and visualization tools. Start getting more comfortable with Github and basic bash scripting. Begin learning R or Python and moving beyond descriptive analysis to predictive.

      4. Data Scientist - If you've mastered the prior 3, you're much better positioned to seek out Data Scientist roles.

      I also know of bootcamp style programs in the area, such as Galvanize, where you can get advanced training:

      Throughout your journey, be sure to develop a portfolio of our work - it will go a long way!

      I'd love to meet you. Here's a link to our meetup page so you can catch our next event:

      We often have Data Scientist speak or attend our events so it would be the perfect opportunity to have a candid conversation and get even better advice! :)


  2. There's a term in buddhism called "auspicious coincidence" when the right circumstances come together and we find where we should be. I think your blog is it, and this post.

    I have a liberal arts education and upbringing, and this year I started a job doing voterfile QA. Just today I was talking to one of the senior people that you mention in the Resources for Self-Improvement post, telling him I'm more than 20 years behind in education, more even. Need someone to play a Schubert sonata or analyze a Jane Austen movie adaptation? I'm your girl. Data prep and code? That is not in my knowledge base.

    But, here I am. I do have a master's in library and information science, which kind of bridges the two disciplines. I've been trained to research, comprehend, and self-teach. An important tenet of the librarian profession is continuous learning, and I am putting that to the test right now. It's slow going. There are... holes in my knowledge that "scientists" just can't grasp. And of course, I can't describe what I don't know. Every time I try, it's an exercise in frustration.

    I'm so grateful to have stumbled across your blog today while perusing the Zen Masters page on the tableau community. You give me hope. I can't wait to dive through your blog for your insights.


    1. Hi, Beatriz! Thanks so much for taking the time to share more about yourself. I love when I find moments in my line of work where my non-data training can really shine through and add unexpected value. I wish you all the best as your continue your journey - remember to not be too hard on yourself and know that you already have a lot to offer and everyone always has more to learn.

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    As some of you may know, I co-founded a group in the Bay Area for women who work in the data industry with Chloe Tseng back in March. These past 8 months have been extremely rewarding for me. Not only have I been sharpening my organizing and community building skills but I've built an amazing network of friendship and support.

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  6. Hello! What's the update been on the responses from the survey? I'm constantly thinking about ways to contribute in stem industries (esp data fields) with a business/policy background. So your work's interesting to me!

  7. Nice blog. Really helpful for learningTableau and keep update on some more tutorials….. I liked your blog.

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