During the orientation for the new University of Washington data science certificate program, we were prompted to answer several questions about our background and what we were looking for out of the program.  After answering these questions and hearing the responses of others, I felt that the question of our background in data science deserved a more detailed response and discussion than was practical during the orientation.

What is data science?

Before tackling the question of my background in data science, I want to make clear what I see data science to be.

I feel very strongly that data science is finding patterns in data.  It is more than just a collection of concepts like “big data” or technologies like Hadoop. It is a way of approaching the world, identifying data sets that you encounter and the patterns contained within.  Just like the scientific method, it has formalized components, but can be practiced informally in all parts of your life.

My background in data science

While I have not had any formal training in data science (it being a relatively new field), I see data science everywhere I look in my background.

While studying to be a biochemist, I was drawn to classes that involved programming, data processing and statistics. Analytical chemistry in particular was a great match for me because it was entirely focused on how to collect data and extract meaning from it.  I didn’t know it at the time, but I was already becoming a data scientist.

While doing research after graduation, I was frequently the go-to person for coming up with ways to analyze data and for troubleshooting problems where the data we were collecting was not what we expected.  Again, I was practicing data science, but doing so with rather crude tools.

When I worked as a lab technician, I was much less useful at executing a set procedure than I was at identifying patterns from systematic errors in our tests and how we could fix them.

Switching career paths from Chemistry to database administration seemed odd to many of my potential employers, but felt totally natural to me.  In both career paths, I focused on collecting data, finding patterns, and correcting systematic problems.  The only differences were the technologies I used and the kind of data I was looking at.