Technology is omnipresent. And when it comes to imparting engineering education, technology is the meat and potatoes. Gone are the days of traditional teaching methods practiced within the walls of a classroom, following a set of particular curriculum. They have become a history. These days, technology-powered smart classes are in – they keep students enticed and hooked into learning. Laptops, smartphones and tablets have made gaining access to knowledge anywhere anytime downright easy. Not only that, access to education has enhanced versatility in the form of videos, audios and images that are available right at our fingertips through smartphones and tablets.
Technology is taking a new shape, each day. None other than ace modern engineers and scientists understands this better, and as a result, they try adopting innovative technologies for better, powerful future harnessing newer opportunities.
Data is the most influential strategic asset for companies in a data-powered economy. Data is used to measure the ability of a business to perform notable tasks and operations, and draw significant insights through complex machine learning algorithms.
Gaining access to data is not a problem; but the real issue lies in having the right kind of data that helps companies remain on edge. A large number of them don’t even realize they are supplied with chunks and chunks of bad data, punched with wrong formatting, plenty of duplicates, having missing fields or irrelevant information.
Suppose, you have two resumes of two data scientists in your hands: A and B. Both of them possess similar backgrounds and expertise: qualifications, platforms, languages, frameworks, methodologies, industries and more. Theoretically, they are more like the same person – on paper. Yet there are few things that reflect that A is more successful than B – but how you determine that?
Here we’ve whittled down a set of habits or traits of successful data scientists that make them stand out from the rest of the pack.
According to a recently published report from McKinsey – “Alphabet invested roughly $30 billion in developing AI technologies, and Baidu, which is the Chinese equivalent of Alphabet, invested $20 billion in AI last year.”
Not only companies, but reports suggest Chinese government is pursuing AI technology relentlessly in an attempt to drive the AI innovation, singlehandedly.
A general consensus: the scene of employment is changing. The jobs in data science are spiking up, and at a robust rate. According to World Economic Forum in 2016, a nuanced state of affairs with employment fluctuations is likely to happen across sectors, jobs and geography in the coming years – hold your horses and wait with bated breath!
Job Opportunities till 2020
A wide set of factors are expected to bring upon different effects on the varying segments of employment market till 2020. For an instance, recent demographic stats in the emerging job market are likely to ace up employment by 5% approx worldwide. On the other side, the surging geopolitical instability across the globe could reduce employment by 2.7%. Amidst this, artificial intelligence, touted as a replacement key for manpower is likely to have a minute effect on job reduction by a mere 1.5%.
Considerably, the overall figure points that the computing and mathematical jobs are going to increase by 3.2% – because a sturdy compilation of technological and geopolitical instability effect is expected to generate an altogether positive effect across various employment chains, suggesting the instability will in return result in a higher demand for programming, computing and modeling.
However, recruitment procedure is going to get more challenging.
Following the latest trends, the applications to universities by students have taken a halt – in UK, the number of people applying to universities has fallen drastically – the reason anticipated is the result of Brexit.
But irrespective of any reason, lower application rate is going to affect graduate recruitment. The emergence of a gig economy is largely considered a positive effort, but a lack of benefits like annual leave may cause some hindrance in the effectuality. Also, AI is resulting in a less number of job generation, the automation of entry-level jobs mean lesser jobs.
Hone your skills further after employment
While undergraduates and postgraduates eyes employment as the end of their education, for employers it’s an entirely different ball game. For them, employment is the just a stepping stone in the process of ongoing training to make sure the fresh workforce develops cutting-edge skills. This stands true especially in complex job areas of data science, where a shortage of graduates exists. As a result, motivate your existing workforce to develop required data analytics skills in the most accomplishing way to garner expertise and thorough know-how.
To get the best kind of data science online training, drop by DexLab Analytics Delhi – it is a prime learning platform in India that helps you remain up-to-date with the latest tools and trends. The field of data analytics is evolving rapidly and continuing professional development is the need of the hour.
Source– blogs.sas.com
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More is always better, isn’t it? But does it always holds true, especially when it comes to customer data? Maybe not, because business is all about extracting meaningful insights from data, and if that cannot be acted upon then it is of no good.
Recently, Accenture concluded that one of the greatest challenges that marketers face nowadays is to discover the right ways to turn data into productive insights and then into action. For that, you would need analytics professionals who do know how to collect, store and integrate information, while mastering the technology aspect.
With state-of-the-art technology looming on the horizon, the $150-billion Indian IT industry has a high appetite for workers accomplished in the fields, like AI, Data Science, Big Data, and more.
Soon, it wouldn’t be enough to flash an engineering degree or some minor knowledge in Java or Python – the need for data science and artificial intelligence is on the rise. Automation is going to be the key to change. Globally, 12% of employers have started thinking of downsizing their workforce owing to technological advancement. Amidst all this, don’t think India would be spared. Indian bosses fear automation will reduce their headcount too. But fret not, it’s not all a bad news – there is always a silver lining after rains and that is Big Data jobs.
Shine bright with Big Data
In India, the number of job openings in the Analytics field almost doubled from the last year. Digital natives, like Amazon, Citi, HCL, IBM, and Accenture are waiting to fill close to 50000 positions, according to a study conducted by Analytics India Magazine and Edvancer. All these definitely signify parting off the dark clouds, and I can’t agree more!
Artificial Intelligence and Machine Learning are building a base of its own. Moreover, AI is deemed to be the hottest technical sector in the next 5 years and would beam in success. Along with top-of-the-line tech firms, more than 170 startups have transfixed their gaze on this field. To surf on the next wave of IT jobs, candidates need to step aside from low-in-demand stale skills to excel on budding Analytics skills. Every single HR Manager out there is seeking professionals who can manipulate algorithms and work wonders in various machine-learning models and you can be one of them!
Get better, get evolved
Expertise in languages, like Java/C/C++ gives you a certain edge, but to enter the dominating field of Big Data, techies will be asked to master intricate languages, such as Scala and Hive that are less conventional. Millennial recruiters are also looking out for those who have a keen insight for good design and flawless code architecture. “Programmers who focus on good design principals are always preferred over programmers who can just code,” Rajat Vashishta, founder of Falcon Minds, a resume consulting firm, says. “User experience matters a lot more than it used to, say, five years ago.”
Where skills in technology, like business intelligence, artificial intelligence, machine learning and DevOps are flourishing, minute attention need to be given on proper implementation of these skills, according to Aditya Narayan Mishra, chief executive officer of CIEL HR Services, a recruitment firm, otherwise all of it would be a total waste.
It’s all in the layout
Presentation matters, you agree or not! Make your resume ready to strike the job criteria you are applying for. For example, if a user interface developer wants to become a full stack developer, he must mention back-end programming skills in the profile. This will give an instant boost to the resume. The design of a resume has also changed over the years. Now, the shorter your resume the better response you get. “Most techies write pages and pages of projects in their resumes. While it is important, in most cases, the same information gets repeated. Anything above two pages is a big no,” says Vashishta.
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With the Fourth Industrial Revolution looming ahead, many would think that we are already in a digital economy era. Well, somewhat it holds true even. There are countless new apps and software programmes that help people hail a cab, make reservations in a hotel or mop floors by using robotic technology. Smart machines have become really smart to do a plethora of highly adept jobs, which would have been a little bit difficult on the part of humans to perform.
“While technology has long been developed to serve specific business needs, we are now in an era where people are central to the design and development of technologies,” stated Bhaskar Ghosh, group chief executive, Accenture Technology Services. In a recent interview with a leading financial magazine, he talked over Accenture’s Technology Vision 2017 and gave snippets about the latest trends and innovations that have become a pre-requisite to achieve success in the more-than-ever digitised economy.
The noteworthy triumphs over us, humans, in Poker, GO, speech recognition, language translation, image identification and virtual assistance have enhanced the market of AI, machine learning and neural networks, triggering exponential razzmatazz of Apple (#1 as of February 17), Google (#2), Microsoft (#3), Amazon (#5), and Facebook (#6). While these digital natives command the daily headlines, a tug of war has been boiling of late between two ace developers – Equifax and SAS – the former is busy in developing deep learning tools to refine credit scoring, and the latter is adding new deep learning functionality to its bouquet of data mining tools and providing a deep learning API.
