Today, many technologies let companies collect huge volumes of data. The ROI on these technologies depends largely on the actions taken from the data gathered. Companies are transforming and increasingly feel the need to base decisions on solid, reliable data. So it won't surprise you to learn that the data scientist role is more in demand than ever within organizations.
What is a data scientist?
The data scientist's role is to manage, analyze, and interpret data while accounting for organizational realities, which requires strong business acumen.
3 core skill areas of a data scientist
Technical skills:
The data scientist must have programming knowledge in languages like R or Python, as well as expertise in IT architecture and databases. They need to adapt to different computing environments and have the intellectual agility to keep learning and adopting new methods. They must master data manipulation and be comfortable with several different data structures.
Analytical skills:
The data scientist role requires expertise in solving complex problems. This skill category includes advanced statistics, machine learning, advanced mathematics, modeling, simulations, artificial intelligence, and more. Generally, fields of study in science, technology, engineering, mathematics, and physics help develop the analytical skills sought and provide practice in scientific problem solving.
Business skills:
The data scientist must absolutely understand the corporate environment in which the data lives. In data science, successful projects are the ones grounded in a precise situation and ending with concrete solutions that can be integrated into the work environment.
Data scientists are more in demand than ever. We shouldn't lose sight, however, of the fact that their success first and foremost requires reliable data in sufficient quantity. Real-time production monitoring or data analytics solutions are definitely avenues worth considering!
Source: Data Scientist Skill Set, Data Science Central.
