Though data science is proving itself to be an invaluable tool for organisations of all kinds, very few people complete studies in data science specifically, leaving quite a skills gap to bridge. As a result, professionals from a variety of backgrounds, including financial services, marketing, business, and the public sector, have sought to transition into this exciting and lucrative new area.
While people from all walks of life can become data scientists, individuals with educational backgrounds in science have frequently proven to be a great fit for the role. The skills and experience possessed by scientists have been found to serve as an ideal foundation for data analysis. By completing a training program to learn the specific applied skills of the sector, a professional with a science background can expect to become quite the asset.
Curious about what makes the science to data science transition so natural? Here's what you need to know.
Scientists Are Used to Working With Large Quantities of Data
One of the big advantages that a science background provides is that it tends to involve learning how to acquire and analyse large quantities of data. This is true of physics, chemistry, neuroscience, and many more disciplines in the hard sciences where rigorous experimentation is conducted in pursuit of quantitative findings.
In effect, data science follows this same model on what tends to be a larger scale, with graduates of data science fundamentals courses applying their skills to finding hidden trends and insights within extremely large data sets. If you've studied in the sciences, you should have little difficulty getting to grips with this kind of large-scale data science work.
Experience working with quantitative data in the sciences translates well to data science
Some Scientific Disciplines Rely Heavily on Algorithms, Which are Valuable in Data Science
Algorithms are important tools in data science, serving as sets of instructions that allow for fast and efficient solutions to be found for problems that are being addressed. Skill in producing algorithms is something of a speciality, though, which is part of why organisations hoping to pick up more data scientists often run into difficulty finding enough eligible candidates.
Professionals with scientific training, and particularly from branches like physics, often have experience producing algorithms that makes them appealing prospects for data science roles. The algorithms in data science tend not to be overly complicated, which allows experienced scientists to step up and achieve great results in short order. Even for those without direct experience working with algorithms, a course in data science fundamentals will be enough to achieve a working knowledge of what is required quite quickly.
Scientists Taking on Data Science Fundamentals Already Know the Value of Accuracy
There's a sense of "good enough is good enough" that permeates much of the private sector, and this is an attitude that can be costly in data science. The point of sifting through enormous amounts of data is to find actionable insights that can be used to push an organisation forward to the best opportunities, based not in subjective feeling but in objective information. Achieving this aim demands that data scientists do their best to ensure accuracy.
Good scientists are very careful about achieving accurate results, well aware that review processes and a need for reproducibility necessitates the utmost care be taken in finding measurements and data that truly work. It's a mindset that translates well into a data science context, and position individuals with a science background as great candidates for learning data science skills.
The urge to achieve accurate results gives scientists a great mentality for data science
Are you a scientist who wants to learn the fundamentals of data science?
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