Interesting, exciting, ground-breaking…these aren’t the words you’d normally associate with data. Numbers are, to most of us, a foreign language. But to others, sets of data and the need to problem-solve is an art. If you’re one of these data crafting gurus, chances are companies are looking for you and your number-crunching abilities to help them strategise their next moves.
Today’s market is changing in incredible ways with an increased buzz around AI and machine learning. Data science assists these new technologies by figuring out solutions to problems by linking similar data for future use. An example of this is Facebook’s facial recognition system which, over time, gathers a lot of data about existing users and applies the same techniques for facial recognition with new users. As tools continue to evolve, data science techniques are maturing and becoming more openly used by the average user. While not everyone will be an analytics whiz or a data genius, the techniques are going to become more accessible and available to professionals who may not be as technical.
Most Data Scientists look at data science as bringing out their inner detective, immersing themselves within the problem and using interesting mathematical algorithms to explore large and, in most cases, complicated data sets. Data science roles are extremely well-paid and versatile in terms of industries and tools. Every company will have a different take on the roles and responsibilities; some treat their Data Scientists as Data Analysts or combine their duties with Data Engineers, whilst others need top-level analytics experts skilled in intense machine learning and data visualisations.
Luckily, the data science industry’s job market is hot today, which means that one way to better understand the meaning behind different data science roles is by analysing all job offerings, their description, and their skill requirements. These listings often go into great detail and help to answer questions such as “How similar or different are certain roles?”, “What technology stack do I need to master?” and “What is the mindset that I need when accepting this job?”.
But the real question you’re probably asking yourself is… What does it mean to be a Data Scientist? And why is it so important in today’s market? Well, science has really changed our concept of technology. Businesses have it a lot easier compared to 10 years ago. Data science has bridged fiction and technology. Right from LinkedIn to Tinder, data science is being used everywhere. Institutions are opening their doors to data science and unlocking its true potential, thus increasing the value of a data scientist who knows how to drive the value of large data sets within an institution.
So, if you’re flirting with the idea of becoming a data professional or even a little more involved in data science then here are the technical and non-technical skills needed; Preferably a PHD or Master’s Degree in Applied Mathematics, Statistics, Computer Science or Engineering, Statistical Analysis, Data Mining and Processing, Programming Skills and Other Analytical Tools, Adept at working with Unstructured Data, A strong Business Acumen, Strong Communication Skills, Great Data Intuition.
Please reach out to our specialist data team to understand more regarding a career in data: https://www.weareaspire.com/data-analytics-team
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