Data Science


What is this datial intelligence technique known as deep learning is white-hot right now, as we have noted numerous times before? It’s powering many of the advances in computer vision, voice recognition, and text analysis at companies including Google, Facebook, Microsoft, and Baidu, and has been the technological foundation of many startups (some of which were acquired before even releasing a product). The field of data science in Houston is emerging at the intersection of the fields of social science and statistics, information and computer science, and design.

As far as machine learning goes, these public successes receive a lot of media attention.

But they’re only the public face of a field that appears to be growing like mad beneath the surface. So much research is happening at places that are not large web companies, and even most of the large web companies’ work goes unreported. Big breakthroughs and ImageNet records get the attention, but there’s progress being made all the time.

For all the talk about big data and how it can help us track down needles in haystacks, there’s still a lot of work to do when it comes to issues like public health. When successful intervention might require timelines of minutes or hours rather than days, it takes a might keen eye to monitor lots of needles in lots of haystacks and, more importantly, spot new and important ones as they pop up.

We’ve been following the news out of the Global Database of Events, Languages and Tones (GDELT) project for the past several months, and it’s very impressive as a tool for historical analysis of the world’s happenings. It takes and indexes real-time streams from news sources around the world, and now includes hundreds of millions of data points spanning the past 35 years. It has been used for all sorts of analyses so far, ranging from tracking the spread of terrorists…

Interesting to note that Big Data has almost moved from the “Peak of Inflated Expectations” into the “Trough of Disillusionment”.

Google on Monday released the latest in a string of text datasets designed to make it easier for people outside its hallowed walls to build applications that can make sense of all the words surrounding them.

Essentially, the goal with the dataset is to give researchers a base understanding of which entities are important…

In this series, we’ll suggest how to

• Create a case for data science and its benefits to the organization

• Define the appropriate skillsets and implement the right leadership role for data science

• Create strategies for scoping and converging on reasonable expectations of the team both near- and long-term

• Messaging the benefits of data science to prospective beneficiaries

In this series, we’ll suggest how to

• Determining the right skillsets needed using a skill vs responsibilities matrix

• Suggestions on luring the best talent in an intensely competitive market

• Build an effective distributed prediction platform

• Effectively managing the analytics lifecycle through empirical, job, experience

• Analytics will be diffused throughout the organization

• Likely tools which will enable data scientists to tamp down the meantime to insight

• Hindrances to business and non-analysts users to perform prediction

• How building data science teams will evolve over time