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Data Science News
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Why Is Metadata so Hard? | eagereyes
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Robert Kosara, eagereyes blog
from September 14, 2015
The U.S. Department of Education just released an amazing dataset about the costs of going to college, earnings potential, etc. They’re doing so many things right, it’s really great. But what is still lacking is the metadata, making analysis harder than it needs to be.
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Six Core Data Wrangling Activities
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datanami
from September 14, 2015
… At Trifacta, we think about data wrangling as a process that includes six core activities:
Discovering
Structuring
Cleaning
Enriching
Validating
Publishing
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The Data Science Workflow
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BinaryEdge
from September 15, 2015
When dealing with data, it helps to have a well defined workflow. Specifically, whether we want to perform an analysis with the sole intent of “telling the story” (Data Visualisation/Journalism) or build a system that relies on data to model a certain task (Data Mining), process matters. By defining a methodology in advance, teams are in sync and it is easier to avoid losing time trying to figure out what the next step should be. This enables a faster production of results and publication of materials.
With that in mind, and following the previous blogpost about the Ashley Madison leak data analysis, we saw an opportunity to show the workflow that we are currently using. This workflow is used not only to analyse data leaks (such as the case of AshMad), but also to analyse our own internal data.
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Getting started with open source machine learning
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Opensource.com
from September 14, 2015
… Beyond the project home pages and documentation, there are several excellent sources available to teach the core concepts behind machine learning. While there are hundreds (even thousands) of books and tutorials on ML, I’ve tried to focus on those targeted towards programmers and less on those that are more rigorous or focused too much on the math behind the scenes. While that stuff is important in the long run, it is often impedes engineers in the getting started phase from trying out real systems with real data.
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Why Every College Student Needs To Take Science Courses
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Forbes, Chad Orzel
from September 10, 2015
Students who aren’t already planning to major in science often regard it as a waste of their time … This approach is a major mistake, and having offered some advice to future science majors, let me offer some encouragement for non-scientists facing the prospect of having to take science in college. There are lots of reasons why you should take science, or at least shouldn’t avoid it; here are a few.
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Recommending Recommenders – AVC
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AVC, Fred Wilson
from September 06, 2015
… Web and mobile apps are getting smarter and smarter about each of us and recommending things to us that until recently we had to figure out all by ourselves. It almost seems like recommenders are table stakes these days. You can’t even play in the game unless you can do this sort of thing. And that requires a data science team to sift through all the data on your service and make smart recommendations to your users.
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Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013 – The Lan
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The Lancet
from September 10, 2015
The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.
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Inside Spotify and the future of music – Tech Insider
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Tech Insider
from September 03, 2015
There’s a playlist on Spotify I love called Discover Weekly. It’s updated every Monday with a mix of songs, some I know and some I’ve never heard, crossing into almost every genre with no discernible pattern. Like magic, it just knows what I want to hear.
It’s one of the reasons why I’m listening to Spotify more than ever. And I’m not alone.
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Meet the Hackers Who Are Decrypting Your Brainwaves
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Fast Company
from September 10, 2015
The convergence of budget EEG gear and big-data analysis tools is leading to a revolution in DIY brain research for mind-reading technology.
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Intelligent Machines
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BBC News
from September 15, 2015
Intelligent Machines is a BBC News series looking at AI and robotics
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AI: 15 key moments in the story of artificial intelligence
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BBC iWonder
from September 13, 2015
The quest for artificial intelligence (AI) began over 70 years ago, with the idea that computers would one day be able to think like us. Ambitious predictions attracted generous funding, but after a few decades there was little to show for it.
But, in the last 25 years, new approaches to AI, coupled with advances in technology, mean that we may now be on the brink of realising those pioneers’ dreams.
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The Life Scientific, Nigel Shadbolt
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BBC Radio 4
from April 14, 2015
Sir Nigel Shadbolt, Professor of Artificial Intelligence at Southampton University, believes in the power of open data. With Sir Tim Berners-Lee he persuaded two UK Prime Ministers of the importance of letting us all get our hands on information that’s been collected about us by the government and other organisations. But, this has brought him into conflict with people who think there’s money to be made from this data. And open data raises issues of privacy.
Nigel Shadbolt talks to Jim al-Khalili about how a degree in psychology and philosophy lead to a career researching artificial intelligence and a passion for open data. [audio, 27:46]
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Events
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Data for Good Exchange
At the Data for Good Exchange participants will share success stories, challenges, and visions for the future of applications of data science to problems around social good.
Monday, September 28, at 731 Lexington Ave New York NY. There is no cost to attend.
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CDS News
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TEK Systems Comes to CDS
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NYU Center for Data Science
from September 14, 2015
Last Friday, NYU’s Center for Data Science welcomed two companies, TEK systems, and Medidata Solutions to the first weekly set of career information sessions. These sessions give students networking opportunities to find how their Data Science coursework and internships are applicable in various careers.
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Medidata Comes to CDS
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NYU Center for Data Science
from September 14, 2015
Having sponsored the Facebook smartUP last Spring, as well as sponsoring two more smartUps taking place this Fall and Winter, Medidata has continued to partner with the NYU on multiple fronts. Even one of the Masters of Data Science students from the first graduating class, Patricia Allen, began working as a statistical analyst in June after interning for Medidata Solutions during her studies at the Center for Data Science.
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Women in Data Science: Kathryn Huff
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NYU Center for Data Science
from September 14, 2015
As part of the Moore-Sloan Data Science Initiative’s ongoing commitment to promoting diversity, we are highlighting the work of 5 exceptional women in the field of data science. The first profile in our series is on Katy Huff, a Berkeley Institute for Data Science, Moore/Sloan fellow. … Kathryn Huff is using computer modeling and simulation to eliminate human error in nuclear reactions, and find safer ways to continue the practice of nuclear energy.
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