Hi everyone, and welcome to my first post of 2021! I hope you had a nice time over the holidays and that your year started well, both personally and professionally. For my first post of the year, I decided to write about my personal experience; how it helped me, and what I learned from it.
If you follow me on social media, then you probably know most of my path in education, but for the new readers, I’m going to write a short recap: I have a Bachelor of Science in Biology and I’m currently finishing Master of Science in Molecular Biology. In my country, a Master’s degree is needed for almost any kind of employment and is a condition for applying for a Ph.D. However, only some classes are obligatory once you reach the Master’s, and in the second year, you only need to hit a certain number of ECTS; you can choose any of the classes as you please. You can choose classes that are completely unrelated to each other or a complete “module” or a couple of classes that are dedicated to a certain topic; I chose Computational Biology.
I was always interested in coding, and coding in Biology sounded like such a good idea at the time. I already took another course, titled “Bioinformatics”, where I initially fell in love with this type of work. It was a very different class, as there wasn’t that much factual studying, but rather we had a problem that we had to solve using various online tools. This class was something new and challenging. Choosing that module seemed like a normal continuation of my interests; another very important reason was also that classes weren’t held every day and also weren’t compulsory. Now, I naturally tried to attend as much as possible, but with my illness and doctor’s appointments, not worrying about doctor’s notes and attendance quotas was a bonus.
There are five classes in the Computational Biology module and I chose four of them: Algorithms and Programming, Computational Genomics, Machine Learning and Statistics, and Mathematical Foundations of Computational Biology. Structural Computational Biophysics, the fifth one, honestly didn’t sound as appealing. Most of those classes were held in blocks (only Algorithms for a couple of weeks, then Statistics, then Genomics), with Mathematics being the only one we had every week for the duration of the whole semester.
Very quickly, I realised this may not be it for me; my colleagues got a hang of things quicker than me, and I felt that I’m lacking quite a lot of the prior knowledge, things I should have learned in high school, but my high school course back then didn’t focus on that. There were also memory issues, probably due to rapid changes in the medication I was taking, which was taking a priority above everything else.
The whole module is not perfect (for example, I learned quite a lot of Statistics, but not much about Machine Learning), however, I think it’s quite rewarding, especially since it’s the only opportunity we have to even check out a dry lab. It requires a lot of dedication and a lot of free time; at least now I have a reasonable (beginner’s) understanding of how to use R. What I also had, was the knowledge that sometimes, your first choices may not be the best for you and that it’s quite normal not to be exhilarated about the classes you’re taking. See, if I chose anything else, I would be plagued by the “what if-s” and now, after passing all the classes, I can confidently say I’m happy with the decision I made, but Computational Biology is just not right for me.
I’ve learned a lot and my professors were very understanding, although I honestly believe they also figured out this field isn’t my strength, but they helped me navigate all the tasks anyway. I gained a deeper understanding and appreciation of this type of research and re-discovered my love for the wet lab. I don’t know how much this knowledge will help me in the actual research, but even if I won’t do profound coding, statistical analysis is always an incredibly important skill to have.
If you had a similar experience, don’t be too hard on yourself – sometimes, we have to try out different things, even academically, to realize what kind of research interests us. Of course, at times that can be rather difficult and not everyone has the same options and opportunities. Academia can bring about a lot of stress and pressure, even without us doing the same to ourselves.