Myriad opportunities in Data Science

Data scientists

Myriad opportunities in Data Science

Explaining a host of opportunities in data science, Gaurav Vohra busts some related myths.

Data Science has emerged as one of the most exciting fields in the recent times. Since it is a new field there is both excitement and confusion about it. Data science is a beautiful combination of technology, business, and mathematics that impacts every facet of our life. To list a few, marketers use it to predict the likes and dislikes of their target audience, bankers use it to identify risky customers, sports clubs use it to prevent injuries to their players and presidential candidates use it to improve their fund raising efforts.

This in turn translates into a huge demand for trained professionals in the field of analytics. Gartner estimates that 4.4 million data scientist jobs will be generated by 2015. This is a staggering number, but 2 out of 3 of these requirements will go unrequited due to the lack of skills required for the job of a data scientist.
The vast gap between demand and supply of analytic talent gives people a chance to explore the field of analytics.

Professionals around the globe are seeking to equip themselves with data science skills. As with any emerging field, there is an equal mix of excitement and confusion. Companies are asking ‘How do I select a good data scientist?’ and professionals are asking ‘How do I become a good data scientist?’
There are many opinions floating and perceptions about data science being formed, some of them not entirely accurate.

Myth 1: Data science is a field for math nerds.
This is a myth that comes from a lack of understanding about how data science is applied in business. Data science requires an understanding of statistics and probability because most of the predictive modeling techniques are based on these concepts. However, as a data scientist, one is never going to use statistical formulae to calculate results of complex equations. With the introduction of sophisticated software available, today’s data scientists need to focus on understanding the interpretation of these techniques rather than the mechanics of the application. Sadly, most of our books and courses today focus on the mechanics rather than the interpretation.

Take a simple example. The Chi-square is a popular statistical measure that every data scientist needs to know about. However, the crucial question is what a data scientist needs to know about Chi-square. Most of traditional statistical education focuses on the formula and calculation of Chi-square test whereas that is something a data scientist will never actually use in real life. When the data scientist needs to use the Chi-square test, he or she will use statistical software like Excel or SAS or R that will do this for them. Instead, what the data scientist needs to know is when to use this test and how to interpret the results.

This is the reason why data scientists don’t need to be mathematical geeks. What they need to know about statistics involves more of logic and common sense than pure mathematical ability. While one does need to have some comfort with numbers, the role of logical ability and common sense is often underestimated in a good analysis.
So if someone is interested in data science but intimidated by the mathematical complexity that seems to come with it, they need to think again. With the right mix of logic and common sense, one can go far as a data scientist even if one has moderate mathematical abilities.

Myth 2: Learning a tool is the equivalent of becoming a data scientist
People often equate learning a tool such as SAS with becoming an analyst or a data scientist. This is far from the truth. Learning SAS may make an individual a SAS programmer but not a data scientist. A data scientist needs to go beyond the tool and master other skills such as the application of various predictive modeling techniques as well.

While learning a tool is essential, it is not the only thing one needs to do to become a good data scientist. Most people have gone in for tool-based certifications such as the ones offered by the SAS institute in the hope of an easy entry into the world of analytics. But they have been sadly disappointed.
 Organizations ,while hiring data scientists, are looking for a combination of mathematical, programming, and business skills, not just expertise on a particular tool or software.

Myth 3: Artificial intelligence will replace data scientists soon
The field of data science is surely evolving and there has been a push towards automation in data science. Increasingly, sophisticated algorithms are being built in the hope to eliminate the need for a data scientist.

However, this is not likely to happen. Even with the most sophisticated algorithms, we will still need sound judgment, domain expertise and hard work.
Data scientists are here to stay. Demand for their skills is already sky high. And it will continue to increase in the foreseeable future.

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