In this contributed article, Bobbie Kilberg, President and CEO of the Northern Virginia Technology Council (NVTC), discusses the misconception that tech is all about tech, and which areas employment prospects should be focusing on. More and more businesses and governments are capturing, analyzing and interpreting huge amounts of data to boost organizational performance, promote new discovery and understanding, enhance decision making, and tackle public policy and societal challenges.
In this special guest feature, Michael Liberty, COO and co-founder of Signifyd, argues that an overabundance of data scientists is slowing the growth of machine learning. What’s needed is a better balance of data scientists and engineers who can produce machine learning insights at industrial scale.
In this special guest feature, Devavrat Shah, professor in MIT’s Department of Electrical Engineering and Computer Science, discusses the type of training data scientists need in order to glean the most value from big data.
In this contributed article, Jason Miller, Industrial Applications Engineer at Alpine Data discusses the new PFA standard that takes a good step forward from the previous PMML standard. A shift to PFA has the potential to be a watershed event in predictive analytics.
Data science software maker, Dataiku, recently completed a worldwide survey that asked thousands of companies: how does your organization put data science into production? The results show that most companies using data science have unique challenges that fall into four different profiles: Small Data Teams, Packagers, Industrialization Maniacs, and The Big Data Lab.
In this article, we’ll make sense of data science for those unacquainted with the field and outline a series of 7 easy steps to get up to speed with the technology. In doing so, we’ll highlight the integral steps in the “data science process,” so you can get a good grasp of how data science works and how it is of value to enterprises seeking to maximize the value of their data assets.
The Ash Center for Democratic Governance and Innovation at Harvard Kennedy School released a white paper by Innovations in American Government Fellow Jane Wiseman highlighting the relatively new and evolving role of chief data officers in cities and states across the country in working to improve social outcomes through data-driven government. “Lessons From Leading CDOs: A Framework for Better Civic Analytics,” examines lessons learned from CDOs in America’s biggest cities.
World Programming, a leading industrial analytics and data science platform provider, unveiled new features for data scientists and data science teams including those working within life sciences, pharmaceuticals, and financial services.
In this special technology white paper, From Development to Production Guide – Finding the Common Ground in 9 Steps, you’ll learn how managing a successful data science project requires time, effort, and a great deal of planning. Defining the problems to solve and planning the project’s scope is just the tip of the iceberg, as team members need to fully understand all aspects of a project in order to effectively contribute.
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?