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?
Bigstep, the big data cloud provider, announced the launch of Bigstep DataLab, a solution designed to enable data science and analytics at scale. Bigstep DataLab is an enterprise-ready data research service that gives domain experts, data scientists and BI specialists instant access to powerful software like Apache Spark and Jupyter for easier, more flexible and collaborative ad-hoc data exploration and research.
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.