“Data is a precious thing and will last longer than the systems themselves.” — Tim Berners-Lee
In 1997, C.F. Jeff Wu had given the inaugural lecture which was titled “Statistics = Data Science?” for the appointment he had with H. C. Carver Professorship at the University of Michigan. Well, Data Science is not as new as we think it is, it has been around for over thirty years. The time with data is changing and now it is defining the era of numbers.
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Thought leaders like David Buckingham called -”Data is the new Oil. Data alone will amount to nothing unless it won’t be found, extracted, refined, distributed and monetized. This was where Harvard Business Review termed Data Scientist as the sexiest job of the 21st century.”
And, trust me this is just the beginning. Computers are going to get smaller and smaller day by day. The internet is more than 45 years old as a technology. There are over 8.7 billion devices connected to the internet. Enterprise generated data will exceed 240 exabytes per day by 2020! (1 exabyte = 1,152,921,504,606,846,976 bytes). With all these rapid acceleration, these numbers are enormous and it will keep increasing.
Learn how professionals use number to get industry insight as Data Scientist
How did we arrive at them? And are we ready for it?
The term, “Data Science” was coined in 2001 by William S. Cleveland but the popularity started exploding only in 2010. It was at the time when the “dot-com” bubble took place (1998–2000), hard drives were so cheap people started buying them in order to store large data. Little did they know that they would be requiring larger data sets for storing data, that was where Google, Yahoo, and Amazon invented the new computing architecture called cloud computing.
The thing is, we do not even realize how big data science will turn out to be. Right now, I can think of so many things — it is just a fundamental way of looking at reality and how we need to prepare ourselves.
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This is where we need to concentrate on human ingenuity in order to solve problems. And the only way through it is by bridging the gap towards the pathway to Data Scientist.
As mentioned earlier, C.F. Jeff Wu in his lecture defined statistical work as a trilogy of data collection, data modeling, data analysis, and decision making. While concluding his speech, he termed data science and advocated that the statistics should be renamed as data science, statisticians and data scientists.
So how do we bridge that gap?
- Technology is a double edged-sword — hence learn the right tool for a particular skill set. Which also includes having the ability to operate big data platforms and to run various analytic techniques i.e. Machine Learning by using tools such as R and Python.
- Most Data scientists are often not well equipped to translate the highly technical results of their work into the actionable language of reports and presenting them.
What does it take to become a data scientist?
The actuaries know what kind of data they would need and can define their requirements as well, but if the skill set is not met they won’t be able to relate to it. Looking at the brighter side, a data scientist should know how to extract meaning from and interpret the data, which requires both the tools and methods from statistics and machine learning etc.
- Most of the top experts are placing data science into historical context, making sure of explaining how to use it for innovation in financial services, insurance, and way beyond.
- According to Gartner, by 2019, 90% of large global companies will have to appoint CDOs (Chief Data Officer), in order to help companies businesses manage unstructured data, and use analytics to help maximize the value of the organization’s information.
- The proliferation of metadata management will grow by this year — 2018.
- This 2018, most companies will be using machine learning to go through large data sets and find out the incongruity involved.
This is where one then realizes the pitfalls which accordingly the talent needs to be sourced:
Will data science still remain the sexiest job of the century?
With the rapid and constant evolution of data science technology and tools, one must realize that the technology will keep changing with time. To keep pace with the changing world one must keep a close eye on the developing career trends. While Data science won’t be going anywhere for at least a decade. Moreover, there is a shortage of talent and the demand for data scientist still increases.
This is where we come to play as many have already done before us. As an Indian online re-skilling platform, we aim to make Indian candidates part of the technological trend that is ongoing around the globe. With our primary focus being on creating proficient web developers and data scientists, we aim to build candidates that are equipped to take any challenges that may come rather than trying to fulfill the job requirement criteria of the companies.
There was some research that showed, analyzing bad data can cost a typical organization more than $13 million every year. Therefore, there will always be a demand for individuals who can wipe out bad data that can alter results or lead to inaccurate insights for an organization.
Is it worth ignoring?
No,with the rate how companies are looking to expand and scale up their businesses, it has become a quintessential need for companies to hire efficient candidates. Successful startups like AirBnB, Uber, Mu Sigma, etc heavily used data science models to scale their businesses and also gain insights towards future investments. Even advancements like Mobile Banks introduced by PayTM had Data Science as its core during the introduction of the service.
The World Economic Forum in collaboration with Business Insider has placed Data Scientists at the top of the set of skilled professionals that will dominate the work and business space in the leading 14 economies of the world.
Currently to meet increasing demand of Data Scientist around the world, online re-skilling platforms have become the most accessible resources for professionals who are looking to learn and grow in the industry. As a self-paced platform, we at edWisor, aim to enable candidates to learn while they work at their comfortable pace which turns out to be brownie points for candidates who have been looking for lucrative opportunities. As a platform, our primary focus remains in re-skilling the workforce in India and produce developed candidates in a developing country like ours.
