The past decade has been the breeding ground for some incredible disruptions in the technological world; we’ve come a long way from basic feature phones to smartphones that literally obey your voice. A major chunk of the credit for this rapid development goes to the data explosion witnessed in recent years, what with social media becoming not just a viable but the most popular means of staying connected.
All of a sudden, the business world started taking long strides in the direction of data harnessing to create more effective products and services. This, in turn, enabled major breakthroughs in the fields of machine learning and artificial intelligence, paving the way for an ever-evolving ecosystem fuelled by the two-way interactions between humans and machines.
As such, the field of data science has been garnering a lot of attention and understandably so. Data scientist is one of the most coveted job roles right now; in fact, data scientists are considered rockstars among computer geeks nowadays. If experts are to be believed, the demand for data scientists will only keep growing, all thanks to the sudden shift of trend among organizations towards leveraging their internal and co-dependent data assets to impact their bottom lines.
But what does it take to be a good data scientist? Well, you can’t just go and call yourself one if all you’re good at is playing with data on spreadsheets and charting out graphs from them. It takes much, much more. Let’s have a look at some essential qualities you must possess to make it big in data science.
Data scientists must be critical thinkers; keen and objective analysis is essential to inferring any kind of meaningful information from raw, unprocessed data, which can be pretty hard to tame. Data science makes sense only if it can help make effective business decisions, so the ability to identify which aspects of the data to consider and which to ignore is crucial.
Nevertheless, it is equally important for a data scientist to understand that their data inferences can be far from perfect and not without risks; science and complacency don’t complement each other well, anyway. In essence, data science is all about being able to take a balanced look at the data from multiple angles.
A great deal of a data scientist’s job involves coding, be it to clean the data or to mould it to extract requirement-specific insights. Python and R are the most popular programming languages for data science, while languages like Scala and Clojure aren’t far behind in terms of effectiveness.
Industry experts advise in favour of developing sufficient programming skills to be able to handle the underlying computational as well as modular complexities. Data scientists don’t just work with real-time, unstructured data; they also develop and employ comprehensive statistical models that fit the purpose.
It goes without saying that mathematics is a cardinal pillar for all science streams, and data science is no different. As already mentioned, building relevant statistical models that can aid key business strategies takes up the major part of a data scientist’s day. It is expected of data scientists to leverage their mathematical and statistical proficiency to shape up large volumes of data to the business’s benefit.
While mathematical proficiency is essential for a data scientist, it would all be for nothing if the inferred insights cannot be explained in layman terms to all the stakeholders involved. Communication also involves a good amount of storytelling, if I may, to effectively translate a mathematical result into an actionable insight.
A data scientist must be capable of distilling tough technical jargon into something that, while being complete and accurate, is easy to present. Hence, ensuring that your audience gets everything you present — from the problem statement to the result and consequent insights — is essential.
No one is a stranger to the dynamic nature of technology; it is an ever-evolving domain where nothing stays relevant for too long. What is relevant today may soon become redundant and, who’s to say, pointless. In such circumstances, it is but natural for a data scientist to keep apace of the latest trends in technology to utilize it effectively.
While it is essential for data scientists to have a thorough understanding of the problem to be solved, sharp technical awareness is the key to solving the problem. A data scientist must have a clear idea of the computational and system boundaries as well as customer maturity to be able to design tailor-made solutions.
In addition to all the qualities mentioned in this article, a data scientist must be a keen problem solver with a knack for creativity and a passion for continual improvement. Also, a good understanding of the business environment goes a long way in helping design solutions that can effectively aid decision making.
While there’s no saying of the leaps and bounds that data science as a stream is going to take in the coming years, the current situation isn’t all that cheery. There’s a huge gap between the number of opportunities and the number of good data scientists available. Add to it the fact that data science was used as a buzzword a few years ago, there’s no dearth of under-qualified professionals in the industry.
However, given the industry’s dynamic nature and the need for robust solutions, a well-trained data scientist can go a long way and may even prove to be pivotal in carving the future. That said, honing one’s talents as a data scientist is the first step and this end-to-end library of learning resources in the field of AI and data science can really help you make it as a seasoned professional. Happy learning!