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.
Continue reading “How AI Is Going To Change the Marketplace: Top 6 Trends to Fix Your Eyes in 2018”
Amid growing anxiety over machines replacing human intelligence, Indian IT sector is seeking out expert skills in new technologies, involving big data, artificial intelligence and machine learning. There exists a high demand for skills in such newer streams of technology, which now forms the backbone of businesses. It’s not like these jobs appeared out of thin air; after being labeled as “niche skills” for several years, they are now making their way into the mainstream industry.
This year, this trend is going to gain more momentum. It is expected to create 180000 to 200000 new jobs in 2018, mostly related to these new technologies – Alka Dhingra, the general manager of IT staffing at TeamLease Services, stated. It is equally applicable for both large service organizations as well as budding startups.
Here’re the notable areas, where job creation will hit in 2018:
Almost all Indian IT bigwigs are going gaga over AI. TCS, Infosys, Flipkart – nearly all native companies have started delving deeper into data to scale up their business operations and secure success in the future.
Though the world is being ruled by MACHINES, at the same time it’s HUMANS who train machines the way they function to perform human-like tasks. For that reason, the country’s IT unicorn, Tata Consultancy Services has trained more than 200000 employees about IoT and AI. Also, last month, India’s e-commerce giant, Flipkart launched an AI-inspired initiative known as AI for India, through which it has planned to leverage all the data it has gathered over the last few years to frame robust AI-driven solutions that will boost their operational activities further. Millions of dollars are being invested in this program – a company representative shared.
All this is going to need professionals skilled in the domains of deep learning, natural language processing and machine learning – look up to DexLab Analytics for data science online courses.
For several years, native internet companies have been accumulating massive consumer data, which they now plan to mine it to their best interests. Just like Flipkart’s AI for India initiative, food-delivery-tech startup Swiggy is also working hard on its consumer data so that it can start making deliveries even more efficient and faster.
Some HR experts say that pharmacy analytics – an amalgamation of healthcare and analytics will also generate several new jobs for data scientists this new year– at present, machine learning, data analytics and data scientists’ jobs are the most searched jobs on all leading job portals in India.
While bitcoin and cryptocurrency takes the world by storm, top-notch market specialists predict this advanced field of technology is going to create an exploding number of jobs. Cryptocurrency has already started drawing in a large pool of Indian investors, and legal experts are now asking for regulations.
“There could be regulations (for bitcoin) coming, and hence somebody who knows the subject is going to be in demand,” Aditya Narayan Mishra, CEO of CIEL HR Services, said.
Digital technologies are now omnipotent. All startups and matured companies across every domain are adopting suave digital solutions for various functions, like HR, manufacturing, operations, warehousing and communications. In the same manner, marketing too is not limited to its erstwhile conventional mediums; digital marketing is the new talk of the town.
“With more companies in India wanting to increase their digital presence, there is a visible surge in job searches for digital marketing jobs,” Sashi Kumar, the managing director of jobs portal Indeed India, said.
To learn more about how machine learning and artificial intelligence can help transform your business, enroll in a machine learning training course. DexLab Analytics’ Machine Learning Using Python course is superb; it helps students grasp the concepts better.
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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!
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.
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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Business without data is like surviving without an iota of food and water. Data is indispensible. It plays a significant role in framing suave marketing campaigns, creatively, discerning the actual, productive channels to push data through them.
According to latest reports and analysis, nearly 42% of marketers have introduced more than ten solutions related to data analytics, marketing analysis or customer engagement technologies in a time span of 5 years, in which more than 9% marketers installed more than 20 solutions.
Continue reading “How Data Is Rendering Unrecognizable Change in Marketing”
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.
Continue reading “Why Data Science Matters More Than Data Scientists?”
Keen to have a sweeping view of data science and machine learning as a whole?
Want to crack who is playing tricks with data and what’s happening in and around the budding field of machine learning across industries?
Looking for ways to know how aspiring, young data scientists are breaking into the IT field to invent something new each day?
Hold your breath, tight. The below report showcases few of our intrinsic findings – which we derived from Kaggle’s industry-wide survey. Also, interactive visualizations are on the offer.
A lot of ways are there to nab who’s working with data, but in here we will fix our gaze on the demographic statistics and the background of people who are working in data science.
To kick start our discussion, according to the Kaggle survey, the average age of respondents was 30 years old subject to some variation. The respondents from India were on an average 9 years younger than those from Australia.
Anyone who uses code for data analysis is termed as a data scientist. But how true is this? In the vast realm of data science, there are a series of job titles that can be pegged. For instance, in Iran and Malaysia, the job title of data scientist is not so popular, they like to call data scientists by the name Scientist or Researcher. So, keep a note of it.
While “compensation and benefits” ranked a little lower than “opportunities for professional developments”, the best part remains it can still be considered a reasonable compensation.
Check out how much a standard machine learning engineer brings home to in the US
So, what’s going on in your mind? Should you get your hands on the next formal degree? Normally, most of the data scientists have obtained a full-time master’s degree, even if they haven’t they are at least data analytics’ certified. But professionals who come under a higher salary slab are more likely to possess a doctoral degree.
Largely, logistic regression is used in all the work areas except the domain of Military and Security, because in here Neural Networks are being implemented extensively.
Python was once the most used data analytics tool, but now it is replaced by R.
The original article can be viewed in Kaggle.
Kaggle is an iconic platform for data scientists, allowing ample scope to connect, understand, discover and explore data. For years, Kaggle has been a diverse platform to drag in hundreds of data scientists and machine learning enthusiasts, and is still in the game.
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Analytics is the key to modern business growth. But, developing it as a highly interactive analytical interface is challenging enough to exhaust the time and resources both. As a result, many businesses are shifting their focus to Embedded Analytics (EA) for their operations, workflows and decision-making capabilities. This new breed of analytics, known as Embedded Analytics helps businesses leverage the power of data to process them in the most useful manner.
Continue reading “Embedded Analytics: How it’s Revolutionizing Businesses Today?”
Data fires up everything, nowadays. And data science is gaining exceptional traction in the job world, as data analytics, machine learning, big data, and data mining are fetching relevance in the mainstream tech world. By 2025, it is being expected that data science industry will reach $16 billion in value – this is why landing a job in data science domain is the next big thing!
The skills you will imbibe as a data scientist would be incredible, powerful and extremely valuable. You can easily a bag a dream job in corporate moguls, like Coca-Cola, Uber, Ford Motors and IBM, as well as play a significant role in any pro-social or philanthropic endeavors to make this world a better place to live in.
Check out these extremely interesting fields you could start your career in data science:
No wonder, science and medicine are intricately related to each other. As the technology pushes boundaries, more and more companies are recommitting themselves towards a better public health by nabbing biotechnology. Being a data scientist, you would help in unraveling newer ways of studying large amounts of data – including machine learning, semantic and interactive technologies. Eventually, they would influence treatments, drugs-usage, testing procedures and much more.
Power industry functions on data – and tons of it. Whether it’s about extracting mineral wealth from the earth’s crust or transporting crude oil or planning better storage facilities, the demand for data scientists is on the rise. Just as expanding oil fields ask for humongous amounts of data study, installing and refining cleaner energy production facilities relies on data about the natural environment and ways of modern construction. Data scientists are often given a ring to enhance safety standards and help companies recommit themselves towards better safety and environmental regulations.
Recently, transportation is undergoing a robust change. For example, Tesla paved a new road of development and turned countless heads by unveiling a long-haul truck that could drive on its own. Though it’s not the first time, they are prone to lead the change.
Beyond self-driving vehicle technology, the transportation industry is looking for more efficient ways to preserve and transport energy. These advancements in technology works wonders when combined with better battery technology development – in simple terms, every individual field in transportation industry is believed to benefit from a motley team of data scientists.
The internet is not only about tubes, but all about data. The future of the internet is here, with ever-increasing networks of satellites and user devices establishing communication through blockchain. Though they are yet to be used on large-scale, they have started making news. In situations like this, it would be difficult not to highlight the importance of data science and data architecture as they are becoming major influencers in the internet world. Whenever there is a dire need to make the public aware of a new product, we rely on user data – hence the role of data scientists is the key to a better future.
Today, data science is an interesting field to explore, and it is going to play an integral role as the stride in technology and globalization keeps expanding its base. If you have a keen eye for numbers, charts, patterns and analytics, this niche is perfectly suitable for you.
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Far from the conventional science disciplines, like physics or mathematics, Data Science is a budding discipline: which means there are no proper definition to explain what data science is and what role it does play.
Nevertheless, the internet is full of working definitions of data science. As per Wikipedia, Data Science is
(an) interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics.
To that note, a very important aspect is left behind in this explanation: Data Science is a science first, which means a proper scientific method should be devised to tackle different data science practices. By scientific method, we mean a healthy process of asking questions, collecting information, framing hypothesis and analyzing the results to draw conclusions thereafter.
Go below, the process breakup is as follows..
Start by asking what is the business problem? How to leverage maximum gains? What ways to implement to increase return on investment? The finance industry takes help from data science for myriad reasons. One of the most striking reasons is to enhance the return on investment out of marketing campaigns.
A predictive modeling analyst has access to vast data resources, which eventually makes the entire research and gathering data process much less complex. However, it is only in theory, because rarely data is stored in the desired format an analyst wants, making his job easier.
After getting to the heart and soul of the problem, we start to develop hypotheses. For example, you believe your firm’s profit is leveraged by an optimistic customer reaction towards your product quality and positive advertising capabilities of your firm. Through this example, we explained a nomological network, where you are in a position to infer casualties and correlations. While dealing in Data Science, assessing customer perception is very crucial, and so is the analysis of financial datasets.
Formulating a hypothesis is not enough; a predictive modeler relies on statistical modeling techniques to forecast the future in a probabilistic manner. Keep a note, this doesn’t result in indicating “X will occur”, instead it refers “Given Y, the probability of X occurring is 75%.”
Any proper experiment includes control groups and test, meaning a modeler when preparing a predictive model should divide the dataset so as to ensure availability of few data for testing predictive equation.
Now, if we talk about marketing – consider logistic regression. It offers a probability whether a binary event of interest will take place or not.
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Now is the time to make a decision: do you prefer the quantitative approach? As social media is totally unstructured, the qualitative approach needs to be implemented using Natural Language Processing, which can be a tad difficult. Now, how about making a longitudinal analysis, while transforming data into time series? Do all these questions rake your mind? Yes? Then you are on the right track.
This is the final battle scene for all predictive modelers. It calls for all the documents, based on which a modeler made his decision during the development process. All the assumptions taken have to be identified and highlighted beside the results.
And with it comes the end of our Science in Data Science process!
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