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Surging demand for data scientists

Big Data

The number of electronic devices and sophisticated technologies have been acutely increasing in the last decade, resulting in massive amounts of data a.k.a Big Data. These days, this data is analyzed to provide insights that benefit organizations in various sectors. Here, Big Data analytics is the force behind these organizations that are trying to ‘make sense’ of huge volumes of information. Initially, this was the job of a business analyst. However, the sheer amount of data makes it impossible to analyze using traditional methods.  The use of Big Data in crucial situations has become common and has also become a crucial tool in helping organizations respond effectively. Through the use of computer algorithms and analytics, data science professionals, popularly known as ‘Data Scientists’ have the ability to provide a better understanding of the data and find patterns. These data has the potential to improve the quality of life, safety, health and wellbeing and plays an important role in Defense and in responding to humanitarian causes. A small example of this is, using data to find the true price of parking in Boston. Well, it is no longer the job of clairvoyants to do this (Not that clairvoyants were employed to do this), but the job of Data Science professionals! Looking at all the cool talents, Data Scientist is rightfully called as the ‘Sexiest Job of the 21st century’. They are also looked upon as the rock stars of the tech world. With Big Data bringing in more and more data to be analyzed, data scientists are in huge demand. So much in fact that according to Glassdoor, it ranks #1 in top jobs in the U.S with a job satisfaction rate of 4.4%. surging demand for data scientists Demand for Data Science skills: In a recent survey by Forrester, approximately 40% of analytics decision-makers in various organizations have reported that they have already implemented Big Data technology and are working on expanding it. In addition, 30% of the respondents of the survey are planning to do so in the next year. Data science is witnessing rapid adoption rate, moving up from ‘if it should be used’ to ‘how much should be used’. Moreover, Cloud Computing’s accelerated computing power has further added vigor to data science. We now have the ability to add CPU horsepower when needed to perform complex analysis on large datasets. The increasing demand for public cloud, adoption of Artificial Intelligence, colossal growth of Internet of things (IoT) applications and Machine Learning are driving the demand of data science market. According to the report ‘The Quant Crunch: How The Demand For Data Science Skills Is Disrupting The Job Market’ by IBM in partnership with the Higher Education Forum and Burning Glass Technologies, the demand for data scientists is expected to increase by 28% by the year 2020. Some of the key findings of this report are as follows:
  • Data scientists are offered an average salary of 105,000 USD per annum.
  • Jobs requiring Machine Learning skills and data engineering skills are offered an average salary of 114,000 USD and 117,000 USD per annum.
  • 59% of the demand for data science and analytics skills come from Finance, Insurance, Professional services and IT sectors.
  • By 2020, there will be 700,000 job openings for data scientists, data engineers and data developers.
  • By 2020, the number of job openings for data professionals in the U.S will increase from 364,000 to 2,720,000.
  • By 2020, data savvy professionals will be in huge demand due to the huge investment by tech giants on data science and analytics.
There is surely a huge demand for data professionals but do we have the necessary skilled professionals? When it comes to recruitment, recruiters find it difficult to catch desirable candidates with Machine Learning, Big Data and Data Science skills. According to IBM, it takes an average of five days to find skilled candidates for Data Science and Analytics jobs and an average of 53 days to recruit a competent Analytics Manager. Yoshua Bengio, the head of the Montreal Institute for Learning Algorithms, during a conversation with Business Insider, said, “The growth of demand (for data professionals) is much faster than the rate of which we can produce people with PhDs or even master’s in this area.” In addition, according to Karla Samdahl, Cisco’s global head of executive talent acquisition, “Bleeding-edge technology skills are relatively scarce and highly sought after.” With new technologies like predictive analytics and artificial intelligence taking over, companies are on the lookout for talents that will keep them up-to-date and stay ahead of their competition. Stressing this point, Michael Ross, Visa’s Executive Vice President of HR, says “We are looking for software engineers and data scientists — change agents who want to tackle the transformation of digital commerce enabling payment via taps, dips or swipes on smartphones, tablets and even cars and appliances.” According to Forbes, data science happens to be one of the 14 in-demand tech skills that employers and recruiters are struggling to fill. As a result, candidates with the right skills are practically guaranteed a job and employers looking to adapt the new technology are willing to pay premium salaries for professionals with the right expertise in these areas. Real world applications of Data Science  So, where are these data scientists employable? There is a huge preconception that Data Science or data scientists are employable only in IT and hardcore technology sectors. Nothing could be further from the truth than this. It is agreeable than corporate giants like Facebook and Google are ready to grab any potential candidates proficient in Artificial Intelligence and Machine Learning, however, the need for such expertise extends beyond the tech and business sectors. Here are just few of the scenarios where data scientists are using their ‘rock star’ talents. Recommender Engines: Recommender Engines a.k.a Recommender System is a renowned data science application. They are a subclass of information filtering systems, systems that cut through the noise of all options and present users with just the subset of options that are interesting. Recommender engines provide an intelligent approach when it comes to filtering by presenting information that might otherwise have gone undiscovered. For example, Data scientists at Tendril, an energy software company that provides analytics and consumer solutions to energy suppliers, chose a hybrid approach that combines both collaborative and content- based filtering. Tendril provides insights on which energy products consumers would most likely consider. Sales – CRM Application: Irrespective of the industries, CRM plays a vital role as it predicts which customer is about to make a purchase, which products will be purchased, and which customer will be lost to the organization. An intelligent CRM predicts the above situations and the insights can be used by sales executives to prioritize their tasks. Image Recognition: The automatic tag suggestion feature in Facebook uses face recognition algorithm. Similarly, the barcode scanner in Whatsapp web browser uses a comparable technique. Speech Recognition: Some of the best examples of speech recognition are Google’s voice, Apple’s Siri and Windows’ Cortana. However cool it maybe, this technology still isn’t accurate and research work is going on. Gaming: Games like EA Sports, Zynga, Sony, Nintendo, Activision-Blizzard have taken the gaming experience to a whole new level. All thanks to data science! Games are now designed using Machine Learning algorithms, which adapt themselves as the player moves up levels. Airplane Route Planning: Southwest Airlines, Alaska Airlines are two of the top companies who have incorporated data science to bring changes in their way of working. These airline companies are using data science to predict flight delays, choose which class of airplanes to buy, land directly at the destination or take a halt in between destinations, and come up with an effective loyalty program to retain old customers and to bring in new. Fraud & Risk Detection: Fraud and risk detection is one of the key use cases of data science in the financial sector. This is because organizations were discontented with bad debts and forfeiture each year. Data science was them implemented to curb these losses by analyzing the data for probabilities of risk and default. Self-Driving Cars: Data scientists are behind some of today’s hottest technologies, like self-driving cars and are cashing in on these transformative technologies. Here’s a cool and short video on self-driving cars.
Conclusion As more and more data are generated, the need for data scientists become crucial and more valued than they already are. With organizations realizing the value of understanding Big Data and data scientists having a vital role in defense, healthcare and finances, they will be the key contributor in unlocking the future.  In addition, the demand for Data Engineers and Data Scientists outweighs the supply at the moment, making it a great time to upskill and make use of this tremendous opportunity.

Manu Jeevan

Manu Jeevan is a self-taught data scientist and loves to explain data science concepts in simple terms. You can connect with him on LinkedIn, or email him at manu@bigdataexaminer.com.
Manu Jeevan
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