2017 is shaping up to be an exciting year in Python data development. In this post I’ll give you a flavor of what to expect from my end. In follow up blog posts, I plan to go into more depth about how all the pieces fit together. I have been a bit delinquent in blogging in 2016, since my hands have been quite full doing development and working on the 2nd edition of Python for Data Analysis. I am going to do my best to write more in 2017.
Until now, New York City’s technology community has led a nomadic existence—meetups in startup lounges strewn with pizza, incubators operating out of empty corporate cubicles. There were opportunities to learn, make connections, and find work, but they were often hidden in the gated warrens of “Silicon Alley,” a loose term for the corridor connecting the city’s Flatiron District, Union Square, and Chelsea.
A new 254,000-square-foot project, steps from Union Square and scheduled to break ground in 2018, could change all that. Dubbed 14th @ Irving, it will feature classrooms and meeting spaces on the lower floors and flexible office space, designed for early-stage companies, spread across a dozen higher floors.
In the U.S., women earn about 40% of undergraduate degrees in STEM fields overall, according to a recent study, yet receive less than 20% each of the degrees awarded in computer science, engineering, and physics. That leaves a pretty serious gender gap in one of the most in-demand fields around: data science.
But while widening the so-called “talent pipeline” is one important way to narrow that gap, it’s not the only solution. If girls can be exposed to STEM programs early on in their educational careers, there’s no reason why adult women can’t make the leap into a data-based role later on in their professional ones. In fact, that’s exactly what these three women did—and not from adjacent roles that were heavy on computational skills, but by pivoting out of creative jobs. Here’s how.
U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.
Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.
40% of all the potential value associated with the Internet of Things requires interoperability between IoT systems.
These and many other insights are from the McKinsey Global Institute’s study The Age of Analytics: Competing In A Data-Driven World published in collaboration with McKinsey Analytics this month.
There was, in hindsight, a clear element of risk to Tesla Motors Inc.’s decision to install Autopilot hardware in every car coming off the production line since October 2014. It paid a price, with federal regulators probing the deadly crash of a Model S while in driver-assist mode and critics slamming Tesla for rolling the technology out too soon.
But there was also a reward. The company has collected more than 1.3 billion miles of data from Autopilot-equipped vehicles operating under diverse road and weather conditions around the world. And in the frantic race to roll out the first fully functional autonomous vehicle, that kind of mass, real-world intelligence can be invaluable. In that way, for now, the electric-car maker has a leg up on competitors including Google, General Motors Co. and Uber Technologies Inc.
“There’s no question that Tesla has an advantage,” said Nidhi Kalra, a senior information scientist at the Rand Corporation. “They can learn from a wider range of experiences and at a much faster rate than a company that is testing with trained drivers and employees behind the wheel.”
Boston February 7-9. If you are an academic affiliated to a university, please apply for a free 2-day pass to the Spark Summit; deadline is Monday, January 2.
Deep learning is a fast-changing field at the intersection of computer science and mathematics. It is a relatively new branch of a wider field called machine learning. The goal of machine learning is to teach computers to perform various tasks based on the given data. This guide is for those who know some math, know some programming language and now want to dive deep into deep learning.