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The emergence of data science has many implications for colleges and universities alike. With all the focus that is being put on data science industries, the increase in bandwidth and processing power of data analysis tools has further fuelled a data explosion that has taken almost every sector in its wake. This has made data science one of the most sought-after and lucrative careers in the IT industry and has further spurred the demand for data science course. According to a study performed by the McKinsey Global Institute, companies who emphasize analysis of business data can increase their annual profits by 60%. The report further states that by focusing on data analytics, the healthcare sector can reduce their business operations expenses by 8%, which roughly amounts to $200 billion per year.
Who are data scientists?
Data scientists collect and analyze data from a range of sources to generate meaningful business insights for their organizations to give them a competitive edge in the market. These sources include a company’s engagement data from clickstreams, social media, GPS plots, log files, and sensors. So, what are the skills that recruiters look for in potential data scientists? Industry experts state that the most sought-after data scientists possess a combination of technical expertise, analytical skills, and strong business acumen that enables them to transform raw company data into succinct and actionable insights.
What comprises a data science education:
The industry demand for data scientists has resulted in a proliferation of data science programs, so much that in 2014, there were as many as 120 courses that offered a qualifiable degree in data science in the U.S. alone. Further, ever since being a data scientist has been touted as the smartest job on Earth by the Harvard Business Review, students and professionals alike have flocked towards data science courses by the thousands. So, what is it that makes data science education so attractive?
A data science course enables a student to become proficient in three significant verticals, viz. data manipulation, analytical reasoning, and development of predictive models. The job responsibilities expected from a data scientist are an amalgamation of the above three techniques. Data manipulation deals with the ability of a data scientist to manipulate large chunks of raw data to derive some meaning about the company’s business operations. These insights are then made sense of using analytical skills like statistical modeling and derivation of mean and medians. Finally, the information harvested from data manipulation and mathematical modeling are fed into predictive models to derive actionable insights.
Potential of data science in the present world:
Big data, and in consequence, data science, has a tremendous transformational potential in the current world. When integrated with numerous technologicalinnovations, this potential is only bound to grow. As the volume of data keeps doubling every three years (according to Moore’s Law), the need for experienced data scientists keeps increasing as well. As a result, data science and analytics are now creating a significant impact in the domains of retail, manufacturing, healthcare, and the public sector.
Retail:
One of the fastest-moving industries in the business sector, retail has become highly competitive, especially when technological innovations are continuously driving transformation in the digital landscape. Several online retail companies have demonstrated the use of data science and machine learning in their operations; for example, Amazon’s grocery store by the name of Amazon Go, and Walmart’s Shelf Scanning Robots. Data science and analytics can thus be utilized for a number of applications, which include recognizing potential customers, identifying the key drivers that motivate them to buy more, and to derive strategies that engage these customers most efficiently.
Manufacturing:
Data science tools and techniques are not only related to soft applications, and can also be implemented successfully on the factory floor. Manufacturing companies like General Motors and Ford are actively integrating insights derived from vast quantities of data to create products that are at par with the AI-driven world. From processors and sensors to external and internal sources; data science is being used in almost all the domains to improve production time, boost profits, and reduce energy costs. Predictive analytical models are also being used to predict instances of equipment failure, improve inventory management, prevent accidents, and minimize redundancy.
Healthcare:
The increase in digitized activity that is brought about by the advent of data science techniques has not escaped the attention of the healthcare industry as well. Healthcare organizations and hospitals alike are incorporating these techniques to boost patient care, to collect and research data related to illnesses like Alzheimer’s, Parkinson’s, and other similar diseases.
For fitness enthusiasts, there are a number of digitized tracking devices that collect user data to give them suggestions about their personal health goals. Further, hospitals have now started hiring data scientists to help them fully achieve the benefits of data analytics to optimize and increase patient satisfaction, the efficiency of processes, and staff satisfaction.
When it comes to exploring the prospects of data science in various sectors, the opportunities are endless. According to the industry records of 2009, a typical corporate database consisted of 5,000,000 gigabytes of data. Considering the amount of digital activity that has been generated since then, one can easily imagine how this figure would have increased multifold by now; and add to this figure the amount of data that is being generated continuously, even at the very instant you’re reading this article. Thus, looking at the speed with which data science is driving innovation for almost every sector in the industry, an education in data science has the considerable potential not only in the present world but in the coming future as well.
