From Social Science to Data Science

Quick note: my hope for this particular topic is to start a series of guest written stories of data scientists with untraditional backgrounds. If this is you, and you’d like to guest write, please get in touch!

The most common question I get from college students who are looking to start out in their own careers is how I got from being a Psychology & Interdisciplinary Studies major to being a Federal Data Scientist.

The problem with that question is that we don’t relate (as a society) Social Science or other fields with Data Science, even though data lives everywhere.

But the more helpful response is time, experience, passion, and internal motivation.

Let me back up:

Data Science is a hot topic right now — being able to ask questions about, critically analyze, and disseminate findings about the data we care about is immensely important. Some relatable areas of where big data lives include:

  1. the US Census
  2. COVID tracking
  3. mobile apps like fitness apps, Spotify or iTunes, mood tracking apps

But data also exists on smaller scales in small businesses and non-profits just trying to understand if they’re doing better this year (shout-out to UpMetrics).

In my opinion, data science really has three iterative stages:

  1. Critical Thinking
  2. Analysis
  3. Dissemination

Each of these stages have very different skills, and each of these stages can be hired for. However, where I find the most value, and what I look for when I can refer or hire candidates is when a single individual can do all of these things. Don’t get me wrong, there are major advantages to hiring an expert in each of these areas, and many companies do just this.

If you’re starting off your career in data science — you can always shape your technical skills (and honestly in the next couple decades I believe most people will be required to do so).

The skills you get from a social science background, in which you’ve been able to practically apply your skills (i.e. research, clinical practice) is that you are (1) thinking critically, (2) statistically minded, and (3) able to defend your arguments through speaking.

1. Critical Questioning & Thinking

You’re asking questions about different variables, how they relate and what they relate to. You’re able to think critical about what’s been formerly published and centered in your field(i.e. classics like Freud, Gall’s phrenology, Milgram experiment — some interesting references on the Milgram experiment [1–3]), while understanding the value of their foundation.

2. Statistics

Secondly, you likely have a statistical background, even if it’s minor. The more questions you ask in research, the more interested you’ll likely become in statistics, because it allows you to ask really interesting questions. It even allows you to ask if the questions that we’ve historically been asking actually make sense (i.e. Factor Analysis, mediation and/or moderation models).

3. Dissemination (& Public Speaking)

Lastly, you likely have public speaking experience in defense of a particular background. You are able to stand your ground with a topic and present on that topic — knowing all of the details and counter arguments to the best of your ability, so that you can effectively present your findings.

This you can build through your class presentations at the college level, in presenting research (highly recommend you get involved in research!), and even in your work at non-profits working to spread awareness about your company’s mission or leading events or trainings. By the way, if the thought of a microphone scares you, start small. Plus, much of data science work is currently remote, meaning you’re often doing presentations over videocalls. Even just getting really good at explaining what you do to family members and friends will help you advance at this.

4. Building The Technical Skills

The technical piece is still a requirement to really immerse yourself in a career in data science. If you’re in the social sciences, you likely have some exposure to SPSS, or if your lab lead is more innovative, programming in R. Lean into this, challenge yourself on practicing the scripting and visualizing in innovative ways. Take a course in Python, find peers who are interested in coding. Don’t be afraid to fail. Practice & implement.

Create presentations about the work you’re doing, implement coding along the way, and ask your mentor or lab lead if you can present to faculty or at least to the lab. Get familiar with presenting with both a scientific and a lay audience.

Put yourself out there where your passion is, and you will be well on your way to shaping and refining the skills necessary to transition

from Social Science to Data Science.

Wherever you are in life, you can always learn

Finally, if you’re not a college student and you’re looking to change your career. Everything here applies. You can always go after what you’re passionate about. The beauty about the brain, is that we can continue to make new connections after the brain solidifies and stops growing around age 25 [4]. The key is deliberate practice and attention over time, experience, and more than anything internal motivation and passion around what you’re doing [4].

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

I want to shout-out a few exemplary folks who inspired me in this path:

  1. Alicia Chen (UNC Psych major → clinical psychology research → data science → data engineer) who constantly inspires me to make my own path — LinkedIn
  2. Shannon Hahn (Virginia Commonwealth University → clinical psychology research → William & Mary Experimental Psych MS → federal data science) — LinkedIn
  3. VitalFlo (Luke, Andy, Ryan, Job, Arya) who fostered my growth and gave me a real shot to manage in Data Science

In the posts to come, folks will tell you their own stories on how they got into data science or tech from an non-traditional background.

References

  1. Vendatam, S. (2020, February 24). The Influence You Have: Why We Fail To See Our Power Over Others. Hidden Brain. episode, National Public Radio. https://www.npr.org/transcripts/807758704
  2. NPR Staff.(2013, August 28). Author Interviews: Taking A Closer Look At Milgram’s Shocking Obedience Study. All Things Considered. episode, National Public Radio. https://www.npr.org/transcripts/807758704
  3. (2012, January 9). Who is bad?Radiolab. episode, WNYCStudios. New York, NY. https://www.wnycstudios.org/podcasts/radiolab/episodes/180092-the-bad-show
  4. Giang, V. (2015, April 30). What it takes to change your brain’s patterns after age 25. Fast Company. Retrieved January 9, 2022, from https://www.fastcompany.com/3045424/what-it-takes-to-change-your-brains-patterns-after-age-25

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