“White hat” hackers, the good guys in the hacking universe, have become essential players in global cybersecurity, especially in the private sector. How can the government learn from the private sector when it comes to cybersecurity, and what challenges do they face in working with the good guys?
This past October, prospective and current MIDS students gathered for a Meet & Greet in downtown Denver, Colorado — an opportunity to make connections offline, seek advice on balancing educational and personal priorities and share expertise in the field of data science.
Contributing Author: Dr. Michael Tamir Whether you knew it or not, you’ve probably been taking advantage of the benefits of machine learning for years. Most would find it hard to go a full day without using at least one app or web service driven by machine learning. Amazon, Facebook, Netflix, and, of course, Google all have been using machine learning algorithms to drive searches, recommendations, targeted advertising, and more for well over a decade. In recent years, letting algorithms sort through a company’s data to find out how to optimize a business has become the rule rather than the exception. […]
The torrent of data generated from machines, networks, devices, and data centers in industry verticals provide challenges and opportunities. The challenge is to make this machine data meaningful and actionable to deliver on opportunities around operational efficiencies. On March 15, Beena Ammanath, Vice President of Data Science at General Electric, joined datascience@berkeley to present “Industrial Internet: Big Data and Analytics Driving Big Outcomes.” Ammanath shared real-world case studies demonstrating tangible operational benefits by tightly integrating machines, networked sensors, industrial-strength data, and software to enable intelligent insights and affect measurable outcomes. Here, we provide a recap of her presentation and key takeaways about how the Industrial Internet is making the most of big data to drive big outcomes.
The Master of Information and Data Science (MIDS) program at the UC Berkeley School of Information (I School) culminates with a synthetic capstone project. One capstone team consisting of MIDS students Nicholas Hamlin External link , Natarajan Krishnaswami External link , Glenn Dunmire External link , and Minhchau Dang External link came together to create AidSight External link . AidSight is a platform that uses modern data science techniques to make the 600,000 aid activities reported to the International Aid Transparency Initiative External link (IATI) understandable and digestible at a glance. The AidSight team took some time to answer a few questions we had about the impact their platform is having on the global aid and funding community.
In this 2011 TED Talk, quantum physicist turned writer Michael Nielsen explains the need for a second revolution of open science that translates to the data science world. Due to today’s available technologies, Nielsen sees incredible potential for immediate global collaboration that could advance science at a rate never thought possible.
On December 19th, the fall 2016 datascience@berkeley graduates presented their capstone projects in a public webinar. For the capstone project, the graduates were tasked to solve a real-world situation or problem utilizing their data science skills in communication, problem-solving, influence, and management to provide a fully realized solution.
Machine learning continues to deepen its impact with new platforms that enable more efficient and accurate analysis of big data. One such platform is Apache SystemML External link , which allows for large-scale learning on the underlying Apache Spark External link platform, while maintaining the simple, modular, high-level mathematics at the core of the field. In a recent webinar, Mike Dusenberry External link , an engineer at the IBM Spark Technology Center External link presented the work he and his team are doing to create a deep learning library for SystemML and solve for performant deep learning at scale. Here, we’ll provide the key points that Mike discussed, as well as additional resources for further exploration.
On August 25, the summer 2016 datascience@berkeley graduates presented their capstone projects in a public webinar. For the capstone project, the graduates were tasked to solve a real-world situation or problem utilizing their data science skills in communication, problem-solving, influence, and management to provide a fully realized solution
