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.
Soccer's dynamic nature makes it a notoriously difficult game to predict. But if you have enough data and attributes, you can increase the accuracy of your predictions by using advanced machine learning and artificial intelligence methods.
As the volume of employee data at organizations' disposal increases, novel ways to make use of this data can be defined. By using simple metrics to define your current, past and potential employees, you can increase retention and make sure you hire people who are a good fit culturally.
This post gives an extensive overview of the development process and the capabilities of Infobarris, a tool developed to support the analysis of health and its determinants in the neighborhoods of the city of Barcelona.
There's significant growth in the number of data scientists in the past four years, but this growth is not matched with a growth in the number of data-savvy managers. Will this mismatch lead to problems in the near future?
Data simulators can be an important asset to a company because they can emulate the data you don't have access to, and help you gauge the compatibility of your tools accordingly.
This post dives into different types of text analytics and gives three different examples from the industry to better convey the ideas behind these methods.
Predictive analytics can save lives, and Majid Khattak demonstrates how to do just that by using an index of the El-Nino-Southern Oscillation phenomenon to predict weather patterns that lead to severe storms.
Analyzing traveler feedback on a newly opened airport in Asia, Ravi gives us a walkthrough of his approach to a specific sentiment analysis problem.
Forecasting demand for electricity is of immense importance both for researchers and for industry experts. By accurately forecasting demand, power suppliers can prevent overloads, and can operate more efficiently with a higher profit margin.
A detailed article on HR Analytics, explaining what it is, how it works, and how organizations can leverage it to improve their culture and effectiveness.
Linear programming is a mathematical problem solving technique widely used in various industries to optimize operations and is widely recognized as one of the core concepts of Operations Research. In this post, Professor Lahiri gives a summary of the technicalities of this method.
How Facebook's Ad Matching algorithm works and how marketers can use this knowledge to their advantage.