Few industries are as primed to be radically improved by Machine Learning as the Telecoms industry. About 1.5 trillion U.S. dollars is forecast to be spent globally on telecom services in 2018.
According to a new report by PwC, Artificial Intelligence will contribute as much as $15.7 trillion to the world economy by 2030. That's more than the current combined output of China and India.
Though you probably do not realize it, sophisticated algorithms are already dominating our everyday life, through traffic lights, train schedules, your Facebook newsfeed, and more. An area of algorithmic dominance that often goes unnoticed is in the stock market. These trading algorithms are reshaping the way trading is done on Wall Street. Investors are using algorithms designed for trading to bring greater efficiency to financial markets, and at the same time push us into uncharted financial territory.
By putting into practice the ideas you learn and read about, you can sharpen your technical skills, and develop a critical thinking mindset and creativity to succeed with algorithmic trading.
Today's customer wants a smooth shopping experience in what, where, when, and how he wants to buy irrespective of the retail environment whether it is a physical shop or a digital one. And it is the Internet of Things that is bridging this gap in providing the seamless retail experience to the customer.
The industrial Internet of Things, known as the Industry 4.0 or the Fourth Industrial Revolution, has become a buzzword for automating the smart manufacturing process. It is shaping the future of smart and intelligent manufacturing. No matter what you call it, this connected or smart manufacturing has become all pervasive.
Internet of Things is concerned with the connectivity of devices and sensors, and multiple other sources and services linked to them. It is continuously pushing our society and industries to new heights. These devices, sensors and other connected sources emit enormous and dynamic structured, unstructured, semi-structured and behavioral data in real time, which in itself is raw. This data needs to be churned out to derive actionable insights to improve things connected by IoT and drive overall productivity.
IT services in India is highly vulnerable to the next phase of technological disruption in automation and AI as novel capabilities such as self-repairing code reduce the need for the large-scale deployment of cheap IT professionals. India is likely to witness a crisis like the one it has never seen before.
Experfy announced today that it has been named a "Cool Vendor" in Data Science and Machine Learning by Gartner. The analyst firm recognized Experfy as an important player that "can help data and analytics leaders bring the full data science and machine-learning spectrum to their organizations."
Given the high costs of industrial assets, extending their life cycle by harnessing the power of prognostic analytics for predictive maintenance is essential to maximizing your return on this substantial investment. Leverage your industrial data to lower maintenance costs, increase safety, raise productivity, and improve profits.
Tableau and Power BI offers different advantages and disadvantages in comparing BI solutions. However, need for a Business Intelligence solution for an enterprise depends on the nature and demands of its business niche, size, type and workforce necessities and customer segmentations it caters.
The retail value chain is impacted by Big Data in many profound ways: retailers use Big Data to collect data on their customers so that they can better serve them and market themselves to them; they interact with payment systems whose information must be secured against sophisticated threats; and their logistics and operations can be markedly improved by using Big Data effectively.
The IoT is about a world in which physical objects ranging from gas turbines to mobile phones and television sets are connected to the Internet. That connectivity imbues these devices with intelligence and enables them to collaborate with each other and with people to adapt to the contexts in which we live, work, and play.
In the era of Big Data, as the amount of data available for analysis has exploded exponentially, securing that data has become absolutely essential for corporate success. Firms like Target and Home Depot know first hand the deleterious impact that data breaches can have on the corporate brand and bottom line.
Many Hadoop installations have been focused on individual teams and their particular data analysis projects. But that's changing as Scott Carey points out in "11 Hadoop case studies in the enterprise," business from big banks to airlines and retailers are deploying Hadoop at the enterprise scale. Further he asserts, that "Forrester now says enterprise adoption of Hadoop is 'mandatory,' so any business that wants to derive value from its data should, at the very least, be looking at the technology." Installing Hadoop and enabling a single team to use it has become a simple process, particularly if a cloud-based offering of the platform is acceptable. Whether on Azure, AWS, the Google Cloud Platform or another cloud provider's infrastructure, provisioning a multi-node Hadoop instance can be done quickly...point and click.
Does that mean, however, that the newly-minted Hadoop cluster is ready for the enterprise?
Allstate, the second largest insurance company in United States, recently launched a Machine Learning recruitment challenge to predict the cost and the severity of claims.
Few American industries are as primed to be revolutionized by Big Data as healthcare. In this blog post, Harpreet Singh, the Founder and Co-CEO of Experfy gives us three ways in which Big Data in fundamentally reshaping healthcare.
Data strategy is the one thing many companies need, especially startups, but they don't know it. So, what is a data strategy? How can we define it?
This is a follow-up to the three-part series on 'How to Become a Data Scientist’. It is effectively Part 2a, because it became apparent that the second instalment on ‘Learning’ did not encompass sufficient detail on how to improve the essential, and often overlooked skill of communication.
The rapid growth of Machine Learning and Artificial Intelligence applications is not only in the software domain. As the use of sensors in industrial applications becomes more mainstream, the large amount of data gathered can be used to train algorithms. This, in turn, results in rapid increases in efficiency.
In this article, chemical engineer and researcher Hermes Ribeiro Sant Anna gives an overview of how Machine Learning and Artificial Intelligence are used in industrial production facilities.
This is Part Three in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone interested in this field, irrespective of background and level of experience. The topic of Part Three is: 'The Job Market'.
This is Part Two in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part Two is: "Learning".
This is Part One in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part One is: "What is Data Science?".
This fun-to-read post describes the similarities between implementing a data-driven culture within a sports team and within a company.