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. Jigsaw Academy, a premier online analytics training company, has brought together a panel of experts to dispel the top 3 myths about data science.
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.
It is no wonder that there is a huge demand for trained professionals in this field. Gartner estimates that 4.4 million data scientist jobs will be generated by 2015. This is a staggering number by itself but what is even more interesting is that 2 out of every 3 of these requirements will go unfilled due to a lack of people with the right skills.
This huge gap between demand and supply of analytic talent means that those who do get into the field now stand to benefit a lot. And 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. Jigsaw Academy has brought together a panel of experts to dispel some of the myths that have emerged about data science.
Myth 1: Data science is a field for mathematical geeks.
This is a myth that comes from a lack of understanding about how data science is applied in business. It is true that data science requires an understanding of statistics and probability because most of the predictive modelling 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. That’s all a thing of the past. With the sophisticated software available (paid as well as free) for use, today’s data scientists need to focus on understanding the interpretation of these techniques (when and why to use, and how to interpret the results) rather than the mechanics of the application (how to calculate the z statistic for example). Yet 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 a 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 field is all about 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 learning data science
People often equate learning a tool (such as SAS) with becoming an analyst or a data scientist. This is far from truth. Learning SAS may make you 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 modelling techniques as well.
While learning a tool is always useful (and essential), it is not the only thing you need to do to become a good data scientist. Many 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. Most have been disappointed.
Organizations hiring data scientists do not just look for tool expertise. They are looking for a combination of mathematical, programming and business skills. This is why anyone looking to enter into analytics needs to carefully think about how they are going to acquire all the required skills and not just expertise on a particular tool.
Myth 3: Data scientists will be replaced by artificial intelligence soon
Data science as a field is in its infancy and as it evolves we’ll surely see some activity that is currently done manually, automated in the future (e.g., data cleansing). But a person — with the right qualifications — will always have to be there to tell the machine what to do.
There is a push towards automation in data science. More and more sophisticated algorithms are being built in the hope that they will eliminate the need for a data scientist. However, that 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.
Make the right career move and equip yourself with these in-demand skills. You will see your career graph zoom into the skies.
The ‘Beginner’s guide to analytics’ has been compiled by Jigsaw Academy’s faculty and is a great resource for those looking to build their career in this field.
This article has also been published on the San Francisco Chronicle website.