3 Reasons Biosciences Majors Need to Take Computer Science Courses
Why are you scared of coding? Stop memorizing your amino acids and start building solutions.
I have always been a biology fan and like every other normal person, computers were just these magical machines that I had no idea how they worked. Coding always looked formidable in my eyes, so studying computer science (CS) felt taunting. In high school, I had taken both AP courses for CS to test the waters, but I really didn’t enjoy the content. It was all too boring and just systematic work that didn’t appeal to me. I also thought I would have to spend four years in college staring at my computer if I wanted to study CS. Did anything change? Not exactly. However, I realized that coding was just the easy part of computational thinking and that CS was an extraordinary field of possibilities that could essentially flow into every aspect of life.
In a world that is greatly driven by technology, why shouldn’t we take a step to understand computers and how they work? If they’re capable of achieving so much, why not use them to our advantage?
Coding is difficult and I don’t have time. I’m already halfway done with college anyway. It’s not like I’m going to become a programmer.
These are usually the excuses we have as Biosciences (BIOS) majors. One day I thought, how hard could it be? It’s not like I have to become a pro at computer science. What if I just learned the basics?
Well, the basics were everything.
Besides the topics that are taught, the difference between BIOS and CS is that our curriculum is so focused on learning concepts and memorizing terms that we graduate with lots of textbook knowledge. Do we have tools to utilize in our jobs for the future? Research and lab courses are essentially designed for problem-solving and scientific research, but not everyone in the department wants to go into academia. Are we really benefitting from knowing all 20 amino acid names and structures by heart? What if we spent our time learning concepts for problem-solving and acquiring tools that are useful in BIOS?
Here, I propose that in order to get the most out of a BIOS major you have to do more than just BIOS. Start mixing disciplines and acquire a variety of skills so that you can become a multi-faceted student.
What do our majors mean to us?
In order to understand a little more about why most students choose to enter BIOS or CS, I asked some people from each major this question:
What does your major mean to you?
CS Majors:
- “CS means making a tiny piece of rock solve problems — much faster than any human being can.”
- “CS is the foundation for modern STEM and I think it’s applicable in every area of modern civilization. In a few decades, coding could become a basic skill like writing.”
- “CS theory is mildly interesting to me, but eventually I found out that through software engineering I could build something that millions of people could use.”
BIOS Majors:
- “I’ve always loved Biology and wanted to pursue academia as a career. My major really is something I’m passionate about; a way to keep moving forward along my academic journey.”
- “Biology has always been interesting to me and I couldn’t see myself studying anything else really. Since I was determined to apply to medical school, I found BIOS to be the best option.”
- “At first I thought I would apply to medical school, so I decided to study BIOS. However, in my third year, I decided to switch to Kinesiology because I didn’t want to apply to medical school anymore.”
From looking at these responses, it becomes clearer why most students enter the two different fields. Of course, there are many reasons for this. However, in general, a CS major is intrigued by the power and wide applicability of CS, while BIOS majors are mainly driven by their career choices.
You don’t have to be fascinated by CS and learn about theoretical CS concepts in order to get something out of it. Whether or not students want to enter academia, medicine, industry, or another professional field with their degree in Biosciences, I believe that acquiring computational skills will benefit them regardless of their career path.
3 reasons for taking a Computer Science Course
In reality, computational thinking isn’t just about coding but it’s essentially a process of finding efficient solutions (while thinking like a computer scientist). In fact, the best solutions for problems in BIOS can be created by thinking like both a computer scientist and bioscientist. This is the main point of this blog: encouraging aspiring bioscientists to learn how to also think like a computer scientist. Try to go beyond your expertise!
Now, let’s get to the 3 reasons you should take at least one computer science course before you graduate.
1. Changes Your Perspective
After taking “Introduction to Computational Thinking” in the first semester of my junior year, my career choices and overall perspective of STEM fields drastically changed. This course was essentially built for training students to come up with computational solutions to problems. It developed my creative thinking skills and pushed me to come up with solutions that became more efficient as I gained experience.
The simplest project involved finding the intersection coordinates of 3 different lines. After an entire semester of learning how to use the different tools that Python provided and getting used to coding, we were assigned a project to look through an entire map and find the closest distance between two points. Another project involved making predictions of future sports games based on past data.
Personally, I didn’t find any of the assignments interesting, but the satisfaction that a working program brought me was immaculate. I realized that if I put the effort and time into my program’s code, I would be able to make it function eventually. In fact, I could use this program in a different situation with a few tweaks!
This satisfaction was missing in my experience with BIOS courses. I would spend days and weeks studying for an exam but still not obtain the score that I desired. It honestly felt like I could be using my time more efficiently and productively by constructing code for solving problems in the real world rather than memorizing the entire metabolic system.
Beyond just the appreciation of programming and computational thinking, I started to use the skills I acquired in my life outside of this course. I found it most useful for my research, which is the next reason I will discuss.
2. Research & Academia + Medicine
As an aspiring physician-scientist, I worked in research labs during my undergraduate years and the moment that I decided to learn how to code was after seeing my research mentor (who isn’t a computer scientist) use Python. She used programming for organizing her experimental data and generating amazing figures easily and quickly. While the other lab students would spend hours creating plots, she just had to input her data into her pre-built program, and voila! Beautiful figure ready in seconds.
I knew I had to learn how to do this. The aesthetics and efficiency were too good to be real. So, I took a CS class and learned how to use Python. I had previously learned how to use R for my statistics minor (which is another field I highly recommend for those entering academia or medicine), but Python was just more appealing for me and I hadn’t exactly learned how to “think computationally” — I would just perform statistical analyses using R.
After taking the computer class, everything changed. I started to gain an interest in data science and took more courses on learning how to manage big data. I joined a new research lab to perform virtual research in bioinformatics and being able to use the skills and tools I obtained from my computer science and data science classes was mind-blowing! I wasn’t just using sports data but I could use my skills to analyze genetic data in cancer.
Aside from the endless possibilities of CS in research, coding is also a great asset to have as a physician. A recent study explored whether it would be useful for medical students to learn how to program and they found that it would be beneficial to have an optional training program due to its wide benefits in medical data, for example. If interested, there is a great article for useful tips and resources on learning coding in the medical field.
In general, data management and analysis is a crucial aspect of both academia and medicine, so learning how to program and computationally think is extremely useful. It will make your life much easier!
3. New Career Opportunities
The majority of people who pursue a degree in BIOS either go to graduate school or medical school. It just seems like there are barely any opportunities to pursue besides these two with only a bachelor’s degree in BIOS. This is why the department loses students interested in biology but not graduate/medical school. Not everyone wants to spend more time in school and some can’t afford to, so there should be other opportunities available.
Interestingly, the joining of computer science and biology brings the growing fields of computational biology and bioinformatics. Especially during a time where precision medicine is growing and data science is becoming a widely popular field. You don’t necessarily need a graduate degree to become a data scientist (in biological sciences let’s say). You need the skills and experience.
Some schools have bioinformatics and data science offered as majors or minors, but at schools without these studies, it might be useful to obtain these skills through other CS classes.
It’s easy to say that bioscientists can just hire computer scientists or data scientists to help them with the “tech aspect” of their work. However, if you really want to produce a reliable program, it’s best to have an expert on the data write the program. In other words, if you have a data scientist that also has a degree in Genetics, for example, they’re going to know exactly what to do with the genetic data they have to analyze — this saves time and eliminates major misunderstandings due to a lack of field knowledge.
Overall, with more multidisciplinary studies, students in the BIOS department would consider other career paths like becoming a bioinformatician. The career options expand as you diversify your skills and educational background, so start exploring other fields like CS, data science, or statistics!
In summary, here are 3 reasons why you should take a course in computational thinking if you are majoring in Biosciences.
- Efficiency & Applicability: Computers save time and you could be using self-built programs for any situation in any career path you decide to go into. The sky is the limit!
- More Career Opportunities: You could end up going into a field that works with bioinformatics, computational biology, or data science along with academia or medicine.
- New Skills & Perspective: CS will change your perspective in one way or another because it enables you to analyze situations from a different lens. You also obtain effective skills and tools that are applicable almost anywhere.
Rather than creating an excuse for not exploring another field of “difficult” science, at least attempt to understand it and use it to your advantage! The best ideas come from diversity, so start using computational thinking for finding more efficient solutions to your problems in BIOS. You never know how impactful it will be in your career path.
Thank you so much for reading! I would love to hear your thoughts and feedback :)
