In today’s connected world, data is easier to find, store and use. Think of all the data or information that are available for enterprises from mobile (clicks, visits, interactions), internal systems, social media, email, text, and so on.
All the data collected is unique and come from a different set of demographics, region, and devices. This provides opportunities to enterprises for delivering a one-of-a-kind experience to their potential customers. It also enables them to make informed decisions with the predictive power of analytics, maximize their ROI, and achieve business success.
Data Science is the area of expertise that helps organizations in making sense of the data and make the most of it. It is evident that the impact of data science is profound. But on the flipside, there are certain preconceived notions about data science, which can be a roadblock. That said, let’s look at some of the common myths about data science, which we refuse to believe.
Data science is anything but easy.
While there are no standard set of tools and techniques to master it, you will not even find a fixed educational standard or certifying bodies that can pave the path for a guaranteed successful career as a data scientist. The skills need to be acquired, and although certain tools and technologies can assist you, the learning should always go on. For instance, Hadoop may be one part of your data science arsenal, but you will still need to keep acquiring new skills and gain more knowledge as data science and analytics evolve. This takes us to the next point.
IBM predicts that the demand for data scientists is going to soar 28% by 2020. What does this tell us? Well, to begin with, it presents an acute shortage of workforce needed to handle ever-increasing data. This gap needs to be bridged by training the right professionals, who may not be trained in data science yet and should aim for the right set of skills and perform specific tasks.
Termed as citizen data scientists, these professionals don’t have an advanced degree in the field but can surely master the technology over a period. Hire them to perform some simple as well as moderately complex analytical tasks. They will play a supporting role to your existing data scientists and at the same time, bring their unique skills and expertise to the table.
Another faux pas in data science is the belief that by earning a degree in data science will make you a data scientist automatically.
Yes, you can get a master’s degree with a curriculum that includes course materials related directly or indirectly to data science. But that won’t necessarily propel you towards becoming a professional in the field. You need to work in the field, handle different projects and work on real data to get hands-on experience. The real world is different than textbook learnings, and therefore, it may take some time before you step into the shoes of a data scientist. It is a gradual process that needs consistent efforts.
More often than not, learning a tool is confused with becoming an analyst or a data scientist. But the ground reality is entirely different.
Knowing how to use a tool will make you a programmer or the expert of that technology, but it does not necessarily make you a data scientist. This is because you will also need to master several other nuances such as domain knowledge, statistical understanding, modeling, etc. that are crucial in handling big chunks of data. This doesn’t negate the need to learn tools though. But yes, you need to strike a balance between learning tools and gaining the right skills to become a data scientist in the real sense of the word.
Another myth on our list is that data science is meant for big firms just because it requires expensive hardware, software, and prior expertise. On the contrary, what big enterprises need to look forward to is hiring smart people who have a natural knack for data science and provide them with the appropriate easy to use data science platforms. Such platforms can allow them to apply data science in various processes or at various steps. Therefore, instead of focusing on sophisticated resources and spending all your budget on the same, recruit talented data scientists who can leverage available data.
Data science is open for all industries, and the roles of a data scientist are never limited to one particular domain or niche. Yes, there are some industries such as retail, banking, or transportation that need data science and analytics presented differently. But overall, it is a commonplace in every industry that exists today. If you want to hire a specialist in your industry, you need to take a pick from the data scientists, who already have many years of experience and expertise in the same domain.
Calling It a Day
These are some of the common beliefs (albeit false) related to data science. How many of them were you staunchly following till now? Have any more such wrong notions that need to be added to the list? Bring them on. We’re all ears!
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