Warning: The opinions expressed here are my own and won’t represent everyone’s grad school journey.

Switching career paths is scary and difficult, but not impossible.  Here’s my transition story from astronomy to data science that I hope helps you find your place in the data science community.

I graduated from graduate school in late 2014.  A few months before graduation the idea of staying in academia, doing astronomy research and eventually becoming a professor wasn’t as fun as it was when I first began my graduate degree.  It was hard and intimidating because I had worked very, very hard to have a career in academia.  All throughout my undergrad and graduate years I would travel around the country and abroad to participate in REU or grad student research programs.  It made me excited to visit so many places.  Astronomy took me to Canada, Chile, Germany, Spain and South Africa.


While fun, I slowly started realizing it wasn’t for me. I didn’t have the patience to be a professor, TA-ing taught me that. I wasn’t happy, I became very depressed.  I was nervous. I felt like I didn’t belong there.  I just simply didn’t want to work on one research topic for the rest of my life which is a common thing to do in academia. And, I felt like a failure for wanting to leave the field. I knew I loved data and I loved statistics and I loved programming.  I also I wanted to live close to my family, I wanted a job where I didn’t have to work more than 40 hours a week every week, a job where I could see the results of my work in my own lifetime, a job were I could apply my analytical and programming skills every day.

And as if it was meant to be, that year that I graduated from grad school was the year data science became huge.   A job where you needed coding skills, stat skills and research skills.  I knew I had the qualifications, as a STEM graduate you learn precisely those skills, but how could I ever convince employers I could do the job?

I started by creating my own blog where I would write not only about my personal interest, but also publish independent data projects. I spent months religiously posting analysis after analysis.  I learned new stat models and got extremely comfortable with NLP and other topic modeling techniques.  I created a GitHub where my code was easily accessible. I would promote my projects via Twitter, Facebook and even Instagram to get my data science footprint out there.  Along side building my portfolio, I was arduously applying for jobs. In every single interview, my blog came up.  They’d ask about my projects and I’d explain in detail how I did them, the models, the data.  And my friends, that got me the job.  A regular day for me involves, of course, working with data; cleaning it, formatting it, analyzing it.  I also had to learn tools like SQL, NOSQL to get the data I needed and I talk to business executives often about data driven results and how to apply them.

Today, I still post here regularly, I keep learning, I still love this field and I haven’t looked back.

In summary:

  • get very comfortable with 1-2 programming languages
  • learn your statistics
  • create your online data science footprint

Finding data is surprisingly easy here’s a list of places with data for you to get your hands dirty with:

Posted by:Aisha Pectyo

Astrophysicist turned data rockstar who speaks code and has enough yarn and mod podge to survive a zombie apocalypse.

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