D3295: The importance of data science in ecological entomology

The use of bioinformatics and data science has grown over the past ten years, and it is likely to keep expanding into the future. Thus, highlighting predictive technology was an important part of this study. The use of coding languages, such as R and Python, to create models is quite impactful in S.T.E.M. fields due to their ability to make difficult concepts easier to explain to other researchers, members of the public, and funding agencies (Bastani et all. 2017). Additionally, machine learning has developed as a viable option in use with S.T.E.M. research. Tim Lucus, 2020, explained in his study, A translucent box: interpretable machine learning in ecology, how the development of machine learning has become easier to understand and create. These advancements, Time claims, allow for complex modeling in ecosystems for things like epidemiology and conservation strategies. With these advancements in data science, ecological entomologists can develop new models to better communicate complex phenomena. Additional information previously inaccessible can also be obtained through data science systems and models.