Over the past three decades, there have been trends and shifts in the evolution of the data scientist. Here are four ways the data scientist has changed over the years. Technologies offering advanced analytics were once a domain only large enterprise could afford investing in.
At the root of every model been built, even the simplest, was a solid understanding and foundational rigor of statistical theory. When computing simple statistics or developing descriptive models, the math behind the model, and how this would impact the formation, application, and interpretation.
People are still applying techniques and building models to empirically solve problems, answer question and overcome challenges that improve, reduce error, or otherwise benefit a situation.
Over these three decades, there has been a noticeable trends and shifts - an evolution of sorts - in the foundational underpinnings of development and application within this interesting profession. The following conclusions that illustrate the evolution of the data science function over the past few decades:
1. Data science is more applied than ever. What can be built and fit over a real-life scenario has the dreadful requirement of mattering. Modelling for modelling sake is no longer a thing, and best-fit diagnostics are less important than best-fit for the situation. If a model goes unused, it serves no purpose. One can no longer tolerate or afford the luxury of building models purely for R&D purposes without consideration of utilization.
2. The skill of computer use seems to have taken over the knowledge of applied statistics. Understanding the interior workings of the black box has become less important, unless you are the creator of the black box. Fewer data scientists with truly deep knowledge of statistical methods are kept in the lab creating the black boxes that hopefully get integrated within tools. This is somewhat frustrating for long time data professionals with rigorous statistical background and understanding, but this path may be necessary to truly scale modelling efforts with the volume of data, business questions, and complexities we now must answer.
3. Data scientists are not weird anymore. We are seen as strategic inputs to the decision-making process, and our craft is becoming much more understood. This trend is evidenced by C-level positions at large companies, vertical alignment and paths for data scientists and inclusion at the highest levels, as well as the many academic programs and emphasis now available globally. This appreciation and positioning can sometimes make the field appealing for what seasoned data scientists might call the "wrong reasons" such as corporate fame and value. One could even argue that we really want professionals in the field with a thirst for the truth â?? the science should be about empirically answering questions, and powered by truth-seekers at their heart.
4. Data Science is becoming more widely recognized as both art and science. Understanding the importance of the human - machine integration and complementary decision-making skills from each appears to have made its way more squarely into our field of understanding.
Today's excellent data scientists, especially on the applied side, tend to get things done more quickly, and with improved computer science skills. The need for understanding the inner-workings of the black box seem to balance with the need to know how to run programs, operate tools, and write code.