Data Science as a field has had a massive explosion in notoriety over the past few years, especially since being dubbed as one of the fastest-growing fields in the economy. Since that increase in visibility, individuals have poured into the field and companies have scrambled to snap up available talent. However, this rapid expansion has also left people with a key question in their minds — what next? To help readers better understand how the field of data science will continue to change in the coming years, we’ve turned to Jonathan Cornelissen, a well-known data scientist and visionary in the field.
To help establish the data scientist’s credentials, let’s first take a look at how he’s gained his expertise. Jonathan Cornelissen earned his Ph.D. in Econometrics from the Catholic University of Leuven. During that time, he worked extensively in the field of data science, writing numerous published works that focused on the space. He also spent extensive time working with the R programming language, a popular tool for data scientists to this day. That experience working with the programming language proved trying and became a hurdle he had to overcome in order to continue with his work.
Though the data scientist was able to learn to use R with a diligent approach to his studies, he also found that many of his students were encountering similar troubles in their own efforts. Since the data scientist was already understanding the growing impact that his field was having on the world, he also saw that there was a pressing need to assist people in this difficult area of study. This realization led him to pursue data science education in a number of different ways, most prominently through the creation of an online learning platform that would become one of the standout resources for self-learners in the field.
Now that data science is getting more publicity throughout pop culture and the economy at large, one thing the data scientist expects to see is wider integration of the field’s strategies. Since, at its core, data science is really a set of tools to aid decision-making, this is likely to manifest through a change in the traditional decision-making process. Expect to see companies and organizations move away from the methodologies that have guided decisions in the past, such as a reliance on expert intuition, in favor of more evidence-based approaches.
This ability to guide decisions in a more efficient manner will help to justify the proliferation of data science jobs moving forward as well. This should help to fuel the trend towards increased usage of data science in the wider economy and may also help to allow people outside of the field to further understand its value. Put simply, as professionals outside of the field of data science see more and more examples of the ways in which data-driven decisions can benefit their endeavors, the field as a whole may very well continue to see even further development.
Another thing that time will likely bring to the data science field is a maturation in how it’s structured. As with any new sector of the economy, the first endeavors into data science have sometimes been executed in a less than ideal fashion. This can be due to many factors including an inexperienced workforce and a lack of understanding by non-data science professionals in how to implement the work. This has caused some organizations to bring on data scientists into generalist roles without the necessary infrastructure or direction in place to take advantage of their unique set of skills.
By contrast, as these companies become more familiar with data science as a field, we may very well see further clarification in the roles they fill related to this work. For instance, rather than hiring individuals into a broader “data scientist” position, organizations may refine their needs and bring on new hires in more specific positions. These positions may include titles such as data analyst or data architect, allowing the process of utilizing data for decision-making purposes to be more directed. In this example, the former position would be more focused on pre-processing and interpreting data, whereas the latter would be set on creating overarching data infrastructures with which to complete future work.
Another thing that can be expected as the field of data science matures is an expansion in its ability to complete tasks through increased access to resources. While data has long been a sought after resource for organizations striving to make intelligent decisions, it hasn’t always been valued as highly as it currently is. With more and more organizations realizing the intrinsic worth of data, collection and access to data have increased substantially, with future increases foreseeable on the horizon.
This means that data scientist may very well see their access to data increase in the coming years. Such an increase could mean an improvement in a data scientist’s ability to complete their work and could likewise help lead these individuals in new and useful directions. As quality of data increases along with quantity, the effects of the work engaged in by a typical data scientist may also be heightened, allowing those in the field to produce higher-quality results from which to base decisions. Overall, this type of advancement in the field may have a compounding effect, further impressing upon those in the broader economy the value of a properly trained data scientist with access to a full range of quality resources.
While data science has now firmly established itself as an important part of the modern economy, the field is still in its infancy in many ways. Because of this fact, there are still many exciting advancements on the horizon that could have significant impacts on the ways in which a typical data scientist engages in their work. By looking to the above overview of information from Jonathan Cornelissen, readers should have a greater understanding of how this effect may play out in the near future. Since this field is likely to have a significant impact on the economy for many years to come, it makes for a relevant area of study for practically any professional seeking a deeper look at how their own fields may be likewise affected.