By Altea Lorenzo, Hèctor Perpiñán, Pilar Cacheiro, Silvia Lladosa & Urko Agirre of FreshBiostats
Choosing a professional career is always a challenge: it is a vital process in which we attempt to take into account our own interests, skills and personality, especially when our knowledge about the chosen profession is low. Knowing precisely what courses to take and experiences to line up seems to be one of the most important issues to address during this period.
As noted by different information sources—media and social networks, see here and here—what is now commonly called “data science” is becoming an emergent and fashionable career. Added to that, there are many other reasons for choosing to work in this area and we would like to show you our main motivations to make Biostatistics our future.
From the point of view of a mathematician, one of the main reasons to specialise in Statistics, and particularly in Biostatistics, is the applicability of this field of mathematics. We are sure that other fellow biostatisticians have felt at some point—as we have—the need to apply their mathematical knowledge to real cases beyond the abstraction that characterises the most theoretical areas of this science.
From other perspectives, for example in the case of a biologist, it is often perceived within research groups an unmet need for statistical expertise. This applies both to biomedical sciences and other fields such as ecology and plant or animal biology. In the first scenario, this need might be filled thanks to epidemiology and biostatistics units that provide statistical support to other researchers. In the second one, thanks to the collaboration with statisticians within the host university or institution. In both contexts, a profile of a research biologist with advanced knowledge in statistics would be desirable.
From the point of view of a biostatistician—on the other hand—communication with researchers from other fields like medicine or sociology requires specific training and reciprocal knowledge exchange in these complementary disciplines.
Both points of view seem to suggest that multidisciplinary teams are the most suitable environments for biostatisticians. Having the chance to easily communicate with practitioners from other disciplines makes each day at work a new learning experience.
One other characteristic of Biostatistics that makes it particularly attractive is the fact that there is a wide range of job opportunities in all type organisations, varying from pharmaceutical companies to environmental agencies to hospitals and universities and more.
Having the opportunity to combine work in both the public and private sector throughout one’s professional career is certainly a bonus that provides professionals in the field with a flexible approach and an open perspective, together with the multidisciplinary focus required in this area.
Learning and development of new tools
As mentioned previously, Biostatistics is one of the most applied parts of mathematics. It is nowadays applied to topics that would have been inconceivable in the past. Thanks to the technology available, researchers have been able to develop more specific methods for solving more complex problems. As a consequence and also driven by the inherently curious nature of biostatisticians, new tools are created that bring Biostatistics closer to people without prior knowledge in Statistics or other areas of expertise. This is what makes Biostatistics smart!
Contribution to advances in other sciences
Breakthroughs in Medicine occur largely thanks to the technological advances and their capacity to generate huge amounts of data. Biostatistics also plays an important role in analysing and making sense of that data, and providing the tools to ascertain initial hypotheses and to design potential experiments.
Although there is not a single, easy path to becoming a biostatistician, a firm determination and a bit of work will get you there. If you want to make a difference and you enjoy math and life and health sciences, do not hesitate: choose Biostatistics! Turning data into knowledge, or being even more ambitious, turning data into gold sounds pretty appealing, doesn´t it?