When you hear the term data science or big data, what comes to mind? These terms are both infiltrating and becoming a part of both the academic and industrial world.
Admittedly, they are seemingly both relatively new terms. Yet, the question remains, are they really new terms or have they been in existence for a while? What is big data anyway and what exactly does a data scientist do?
Big data can essentially be defined as large volumes of online data, and recently, offline data as well. Most would conclude that the beginning of big data started with the inception of Google. However, big data is often the online behavior of individuals that are often tracked and collected. This is what is often termed as datafication.
The data is hence collected by various large business companies and later translated into data products. In other words, your behavior online will dictate the type of online product recommendation. For instance, your behavior on YouTube will dictate YouTube recommendations, and the same goes for Facebook and other social platforms.
However, big data is often large and unstructured, and without the knowledge on how to interpret it, it is useless. This is where the need for a data scientist comes in. This brings us back to our previous question: what is a data scientist?
Before we can define what a data scientist is, perhaps it would help if we first defined what data science is. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured.
Hence, it would suffice to conclude that a data scientist partakes in the civil engineering of data. They analyze, evaluate and interpret large and complex data. So, are data scientists just statisticians?
Notably, there is yet to be a data scientist field in the world of academia. Yet, data scientists remain as a highly coveted job title. There is a difference between what we learn and what we do in real life jobs. Hence, are data scientists learning on the job?
If so, statisticians probably have the necessary practical and theoretical foundation for such a job. However, given requirements for a data scientist, a team would work best. A data scientist is expected to be conversant with multiple fields such as computer science, statistics, communication and machine learning just to mention a few.
With a team of different people possessing at least one or two of the required skills, they can achieve the common goal.
A data scientist, as previously mentioned, analyzes and interprets large and complex data. Backed by software engineering skills and persistence, data scientists not only interpret large amounts of data. They also set the data strategy for the company they work for, communicate interpreted data in a clear visualized away.
Hence, they help steer data-driven decision making towards offering better products and services to their customers.