Lab Scientist to Data Scientist: Making the Transition from Biology to Analytics

One of the defining characteristics of the IAA is that its students come from a diverse set of academic backgrounds. While you will find your fair share of math and statistics majors, you’ll also find people who studied history, biology, political science, and engineering.

If you are looking to transition into data science from a completely different field, I want to make one point abundantly clear: you do not need a specific degree in order to be successful at the IAA

I studied biology in college, and if you asked me two years ago what I wanted to do with my degree, I would have told you I was going to get my Ph.D. in cancer biology. During my sophomore year, I started working in a lab where I researched skin cancer, and this experience taught me two important things about myself: I loved learning and wanted a career in science, but I did not want to work in a lab and would not have been happy getting my Ph.D. 

This left me in a bit of a predicament. Towards the beginning of my senior year, I found myself googling the question that every biology major has asked at some point throughout their education: what can you do with a biology degree besides getting a Ph.D. or going to med school? This led me to data science.

Data science has numerous applications in science, especially in medicine. To name a few, it’s used in precision medicine, drug discovery, and medical image analysis.

Below I’ve compiled a list of skills you learn during a scientific degree that apply directly to data science:

  1. Problem solving 

If you’ve ever worked in a lab or done any kind of research, you’ve likely had an experiment fail and had to figure out what went wrong. That skill is applicable in every aspect of data science. Whether you are debugging your code or developing a model, you will know how to work backward to identify the source of a problem, and that is an essential skill. 

  1. Statistical knowledge

Most scientific degrees require at least one statistics course. Statistics is used throughout data science, and having this knowledge will make the transition to data science much easier.

  1. Communicating results

Being able to communicate your results to a non-technical audience is one of the greatest challenges in data science. Previous experience in a scientific field helps develop that skill. If you’ve ever participated in a poster presentation or explained the result of an experiment in a lab meeting, you have experience explaining complex material to an unfamiliar audience, and this is a highly underrated skill that will be used throughout your career as a data scientist

Transitioning to data science from a scientific field may seem difficult, but you are more than capable. The skills you have learned through a scientific education will benefit you, and the IAA faculty/staff will be there to help every step of the way.

Columnist: Sophia Gray