Kelsey Campbell on Becoming a Data Scientist

Tell us about yourself

My name is Kelsey and I am a Data Scientist! I started my career working in public health research, but have made the transition to the tech world in the last couple of years. I currently work at a small software company where I design and implement analytical capabilities for custom software solutions.

On the side I work on projects that combine my data-driven mindset with a passion for social justice. I focus primarily on the LGBTQ+ community, using data to shed light on different aspects of the queer experience. I’m super excited to be heading to Boston at the end of September to be a part of the Queer Health Hackathon, an event that brings together developers, data scientists, and clinical experts to better understand health disparities in the LGBTQ+ community.

Those things take up the majority of my time! Outside of that my hobbies are kind of boring – I like to hike, read, spend time with my partner, etc. Nothing too exciting.

You are an economics major. What prompted you to get into Data Science?

I think it was a pretty natural transition. I was constantly drawn to the econometric, data, and statistical programming aspects of economics, so when I found out there was a career where you could literally do just that, it seemed like a perfect fit! I feel like the skillset is very transferable, and having a social science background is beneficial on teams – we tend to approach things differently than those from more technical backgrounds.

How did you develop your technical skills?

I originally started with Coursera courses (back when they were new and free). I did a Data Analysis course from Johns Hopkins (now part of the “Data Science Specialization”) and a Python course from Rice University. The JHU course was great for introducing methods in an applied enough way that allowed me to really grasp how powerful data can be. The Python course was not data oriented at all, but it was an amazing introduction to the language, and accessible for someone like me who was completely new to object-oriented programming.

In recent years I’ve gotten more and more into data visualization, and this Scott Murray book was a complete game changer for learning D3. I tried soooo many other resources before this one was recommended to me, but just couldn’t seem to grasp how it worked. The way he explains it finally made it click! It is out of date, but, I feel like updating the provided examples to v4 or v5 is actually a really valuable exercise.

One thing I love about the tech field is that there is so much information available online, you can probably teach yourself just about anything!

What are the most important skills to have for someone in your position?

I believe the most important skills for Data Scientists are the squishier ones – having a curious spirit and an open mind, asking the right questions, explaining findings and ideas in an intuitive way for non-data people. The tech aspect can be learned, and has to be learned continuously since it is always changing, but developing a mindset of discovery and creativity will lead to better insights and solutions.

What are some difficulties you faced in your career? How did you overcome them?

I am female-assigned, non-binary, and queer – all things that are not particularly well represented in STEM. I have definitely struggled with how to balance these identities in environments where being authentic and visible is both risky and isolating. It is an evolving process for me, and even as I become more secure with myself and my abilities, I will still have moments where I act in a way to make others comfortable at the expense of myself.

Ultimately though, I feel that it is so important to be the change we want to see in these environments that desperately need to become more inclusive. That means being brave (if it is an option for you) and bringing your whole self to school and work. For me that also means using the privilege I have to sponsor and mentor those who need more support or access to opportunities.

Tell us about Gayta Science

Gayta Science is a site I started last year to highlight the LGBTQ+ experience using data science and analytics! We have a growing team of volunteer analysts, designers, researchers, and developers devoted to investigating a variety of LGBTQ+ issues using data-driven techniques and open source technology. As the team grows we are excited to showcase a wide variety of perspectives, skills, and interests. Projects so far have ranged from serious discrimination and injustice-focused pieces to more fun pop-culture type stuff.

The experience has been extremely rewarding personally. Like I said before, there have been many times in my professional career where my identity and passions have seemed like a disadvantage. For example, in interviews I often felt like I couldn’t talk about relevant data-driven side projects I worked on because the subject matter might not be “acceptable”. Having the chance now to work openly with such an unbelievably talented group of people has been a truly incredible experience.

I am especially proud of a piece I finished recently that used 3 years of personal gender data to explore my genderfluid identity. I was excited to put my story of discovery and acceptance out there, hoping that it would help others on similar journeys, or those interested in expanding their understanding of gender. It was also a fun exercise in text analytics and data visualization.

The site has already become way more successful than I ever imagined, and I feel like it is just getting started! There is just so much to explore since this is a very under-examined domain. There is some great research being done in academia that we amplify, but there is still a need for more accessible insight into the community. Gayta Science aims to be a platform where data is used to enhance understanding, even for non-technical people. Stay tuned!

What advice would you give to someone who is considering becoming a Data Scientist?

I would say to be confident, but never stop learning. Confidence is important because data science is so broad and ill-defined that it is easy to have imposter syndrome, or feel like you’ll never be a “real Data Scientist”. The truth is as long as you are ethically and rigorously obtaining insights from data, you are doing data science! No need to doubt yourself! At the same time though, it is a fast paced field, so you need to be dedicated to constantly learning and improving your skills.

Where can we find you?

You can find me on Twitter. I blog at

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