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The Data Talent Market Continues Its Ascent

We’re at an important inflection point in history where a glaring shortage of data-centric skills, coupled with an increasing demand for data professionals, represents a unique opportunity for those willing to make a commitment to “tool up” or “retool” as the case may be, in preparation for a career in analytics. The good thing is, after all the time and effort, the newly acquired skills will keep on giving because the analytics field shall continue to be in favor for a very long time.

How Alternative Data is Paving the Way for the Future of Investment Management

Many hedge fund managers to mutual funds — and even private equity managers — are turning to alternative data to pave the way for the future of investment management. SparkCognition contends that alternative data has the power to improve valuation of securities and ramp up clarity of the investment process. Download the new report, “Alternative Data for Investment Management,” courtesy of SparkCognition, to learn more about how alt data and machine learning is changing the future of investment management.

What Makes GPUs, GPU Databases Ideal for BI?

What makes GPU databases ideal for BI? That’s what a new white paper from SQream DB wants to explain — incorporating real-world use cases to explain how you can turn your existing BI pipeline into “a more capable, next-generation big data analytics system.” Download the new report, courtesy of SQream DB, to learn more about how GPUs and GPU databases can help you organize and benefit from your next big data analytics system.

“Above the Trend Line” – Your Industry Rumor Central for 5/6/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

TOP 10 insideBIGDATA Articles for April 2019

In this continuing regular feature, we give all our valued readers a monthly heads-up for the top 10 most viewed articles appearing on insideBIGDATA. Over the past several months, we’ve heard from many of our followers that this feature will enable them to catch up with important news and features flowing across our many channels.

CryptoNumerics Announces CN-Protect for Data Science Python Library

CryptoNumerics , a Toronto-based enterprise software company, announced the launch of CN-Protect for Data Science which enables data scientists to implement state-of-the-art privacy protection, such as differential privacy, directly into their data science stack while maintaining analytical value.

Things You Need to Know About Looking for a Data Science Job in 2019

In this contributed article, Avery Phillips indicates there are numerous things to be aware of as you begin the job search for a data science position. Many apply to any traditional job hunt, but others are important for those going into the field of data science to consider. As graduation looms upon you and preparing for a real job becomes more of a reality, here are a few things to take into account.

Pacific Data Science Launches “The Brain” – Automating Back-Office Workflows for Real Estate and Investment Management Companies

Pacific Data Science has launched its newest intelligent solution for commercial real estate and investment companies, The Brain. The Brain was originally developed in collaboration with Paladin Realty Partners, a leading private equity fund manager focused on institutional-quality real estate investments in Latin America. Built with the same reliability and security that Pacific Data Science is known for, The Brain was designed to manage the nuanced lifecycle and automate the complex reporting workflows around real estate investment and development projects.

3 Non-Obvious Keys to Being AI-Ready

Data scientists know what they are doing, and most organizations have no cause to worry about the soundness of their machine learning (ML) algorithms. Where AI readiness typically lags is in other parts of the process. In most organizations today, the process of building, deploying and maintaining AI systems bears no resemblance to traditional IT. Alegion explores three key strategies your business can employ to be AI-ready.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – March 2019

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.