Why should biology students learn coding or data science skills?
Basic experiments in biology, like you, may have done in high school, can take the form of mixing some chemicals in a test tube or simply observing an organism. At more advanced levels, however, much of biology is done using computers.
Learning to code can therefore give biology students a head start in areas like:
Analyzing and interpreting large biological datasets: Like evaluating the physiological effects of administering a pharmaceutical compound to hundreds of experimental subjects.
Modeling complex biological processes: Modern software can simulate biochemical interactions at a molecular level and even help us understand genetics better.
Automating tedious tasks: Applied biological research often requires an experiment to be repeated hundreds of times with minor variations. Robotic and automated analysis tools save time but require programming skills.
Collaborating with other scientists: Cutting-edge research is often interdisciplinary, meaning biologists work alongside chemists, engineers, climate scientists, and other specialists. Interfacing different tools and databases usually require some programming ability.
In today's data-driven and interdisciplinary research environment, coding proficiency is becoming increasingly important in biological research. Additionally, programming skills can be helpful for careers in rapidly developing fields like bioinformatics and computational biology, both of which require an understanding of data science.
Data science benefits biology students by enabling them to analyze and interpret large quantities of data. Data science techniques can help biologists extract insights and knowledge from complex biological datasets, identify patterns and relationships, and build predictive models.
By knowing the fundamentals of data science, biology students can also design experiments that generate high-quality data, clean partially unreliable or irrelevant data sets, transform data from different sources, and communicate scientific findings clearly and compellingly. In addition, data science is becoming increasingly crucial for interdisciplinary research, where biology, computer science, and many other disciplines intersect.
