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Detecting Anomalies in Time Series Data: Deciphering the Noise and Zoning in on the Signals

In this contributed article, Pratap Dangeti, Principal Data Scientist at Subex, discusses how anomaly detection in industrial data is by no means a simple process given the scale at which it needs to happen, and also the highly dynamic nature of business in today’s world. However, it’s still imperative to get it right, as no digital business can hope to stay relevant and competitive in an increasingly tough economy without the power of meaningful data analytics to back its growth.

The Data Scientist Shortage is Huge. Here’s How to Beat It.

In this special guest feature, Roberto Reif, Executive Director, Professional Development at Metis, discusses how the worldwide deficit of data scientists is real, but now that you’ve started thinking about your strategy and how you’ll resource the best people to help you execute it, it won’t seem so intimidating. Keep your eyes on the prize — people who can solve your specific problems — and you’ll be able to win the fight against the data scientist shortage.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – November 2018

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Interview: Rafael Irizarry, Professor of Biostatistics at Harvard University

I recently caught up with Rafael (Rafa) Irizarry, Professor of Biostatistics with the T.H. Chan School of Public Health at Harvard University to hear his unique perspective as an edX instructor regarding the rising demand for data scientists across most industries.

A Cure for Platform Paralysis: Agile Data Science

In this special guest feature, Isaias Sudit, Founder at TROVE Predictive Data Science, proposes that an Agile Data Science platform is right for the enterprise. Agile Data Science is a collaborative process that helps clients identify the best use cases for predictive data science, i.e., those that will deliver the most value.

Dremio Releases Major Update of Data-as-a-Service Platform

Dremio, the Data-as-a-Service Platform company, announced a major release of its open source platform that includes a collaborative data catalog; along with new controls for multi-tenant deployments, end-to-end data encryption, and a breakthrough in performance and efficiency through the Gandiva Initiative for Apache Arrow. These new features support data initiatives by providing shorter lead times, lower operational costs, greater security and governance, and more self-service to a broader range of roles.

Domino Data Lab Announces Domino 3.0 to Power Model-Driven Organizations

Domino Data Lab, provider of an open data science platform, announced Domino 3.0 featuring Domino Launchpad, a module designed to help companies maximize the impact of their data science investments by addressing the operational challenges and bottlenecks they face getting models into production.

Mesosphere Kubernetes Engine Brings Breakthrough Automation & Efficiency for Data-Driven Apps on Multi-Cloud & Edge

Mesosphere, the multi-cloud automation platform company, announced the general availability of Mesosphere Kubernetes Engine (MKE), Mesosphere DC/OS 1.12 and the public beta of Mesosphere Jupyter Service (MJS). Mesosphere Kubernetes Engine is the only software platform that delivers pure Kubernetes-as-a-Service on multi-cloud and edge with high-density resource pooling, yet without the need for virtualization.

The Time Has Come for Data Scientists’ Own GitHub

In this contributed article, email marketer and writer Adelina Benson discusses how data collaboration is the main weakness in the data science world, and, with many actively trying to improve the way in which data is shared, the future looks hopeful. The main issue facing companies in the industry is that there are only a finite amount of data scientists, and so the remit is not as broad as with a general social media site.

The insideBIGDATA IMPACT 50 List for Q4 2018

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!