This is the second article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll discuss the marriage of IoT analytics and the cloud. The cloud is enabling innovation and driving the adoption of many new and powerful technologies, and IoT is no exception. One characteristic of many IoT applications is they generate “too much data.” You don’t necessarily know the value of that data and the process tends to be very elastic.
Here’s a useful new book for data scientists looking to approach the field from a unique perspective that doesn’t include language heavyweights like R and Python. “Julia for Data Science,” by Zacharias Voulgaris, Ph.D. from Technics Publications, allows you to master the Julia language to solve business critical data science challenges. But why look to a relatively new language when you already have other commonly-used languages at your disposal?
Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.
In this special guest feature, John Mertic, Director of Program Management for ODPi and Open Mainframe Project at The Linux Foundation, makes the argument that “When it comes to your data in the cloud, there are certain pieces to the technology puzzle you should have nailed down with five baseline things to address – regardless of your IT, data output, cloud provider and security – before making the switch…”
In this contributed article, Jerome Ternynck, CEO at SmartRecruiters discusses the impact AI and machine learning are having with day-to-day processes for talent acquisition teams. His company recently hosted a Hiring Success meetup and invited recruiting industry experts to share their thoughts on the future of AI and talent acquisition. Here are some of the overarching themes that came out of the event.
In this contributed article, tech writer Linda Gimmeson discusses how today we see big data playing a part in biomedical research, and specifically three main ways that big data has impacted biomedical research include authorization, speedy results, and safer animal testing.
In this special guest feature, Kevin Curran, IEEE Senior Member and senior lecturer in computer science at Ulster University, takes a high-level view of one of the more popular AI-driven technologies we have seen this past year, and will continue to see in 2017 – chatbots.
Contributed by Daniel D. Gutierrez, Managing Editor of insideBIGDATA, this is the first article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this first segment, we’ll set the stage for our discussion by providing an overview of the Internet-of-Things. The Internet of Things (IoT) is an emerging theme with wide technical, social, and economic significance. Consumer products, durable goods, automobiles, industrial equipment, utilities, various sensors, and other everyday devices are being combined with Internet connectivity and powerful analytics capabilities that promise to be transformative in terms of the way we live, work, and play.
In this special guest feature, Matthew Gould, Chief Strategy Officer and Co-Founder of Arria NLG, discusses how NLG doesn’t quite fire the imagination like AI, but it is the means to IA (Intelligence Amplification) and puts humans in control by harnessing computing power to amplify our capabilities.
In this special guest feature, Michele Chambers, EVP Anaconda Business Unit & CMO, Continuum Analytics, discusses how in 2015, President Obama appointed DJ Patil as the first Chief Data Scientist and Deputy Chief Technology Officer for Data Policy to focus on using data to shape policies and practices, and how going forward it is important for all governments to understand that Data Science is crucial to all decision making.