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Data Science News
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10 tools and platforms for data preparation – Data Science Central
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Data Science Central
from September 16, 2015
Traditional approaches to enterprise reporting, analysis and Business Intelligence such as Data Warehousing, upfront modelling and ETL have given way to new, more agile tools and ideas. Within this landscape Data Preparation tools have become very popular for good reason. Data preparation has traditionally been a very manual task and consumed the bulk of most data project’s time. Profiling data, standardising it and transforming it has traditionally been very manual and error prone. This has derailed many Data Warehousing and analysis projects as they become bogged down with infrastructure and consistency issues rather than focusing on the true value add – producing good quality analysis.
Fortunately the latest generation of tools, typically powered by NoSQL technologies take a lot of this pain away.
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Is this the rise of the robots? Probably not | Marketplace.org
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American Public Media, Marketplace
from September 23, 2015
Pedro Domingos thinks the future is virtual. In his book “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World,” Domingos explains that one day each person will have a virtual identity that will make decisions and transactions for them, before they even know about it. He says this will be done through the Master Algorithm, which is an all-encompassing computer algorithm that learns to model your thoughts and behaviors. When it comes to using this technology, companies like Google and Amazon are already a few steps ahead of us. [audio, 5:15]
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5 More Tools All Data Scientists Should Know How to Use
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Galvanize
from September 22, 2015
Even the most knowledgeable data scientists can level up their skills. When it comes to analyzing the data you compile, there are a ton of great tools that can help you get better insights. We talked to our Data Science instructors and put together a list of five more data science tools that you should learn how to use today.
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Trifacta Seeks Truce Between Data Wranglers and IT Chieftains
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datanami
from September 22, 2015
… When it comes to cleansing and managing the data that feed today’s increasingly complex analytic systems, there have been multiple points of view about how best to go about that. To crudely summarize: the business units want independence and the agility to analyze any data as they see fit, while the corporate IT departments prefer standardization, homogenization, and strict adherence to process.
Trifacta has found itself in the unenviable intersection of these camps ever since it set out to build an automated data transformation solution for today’s popular big data platforms, namely Hadoop but also enterprise data warehouses.
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Causal attribution in an era of big time-series data
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The Unofficial Google Data Science Blog
from September 23, 2015
For the first time in the history of statistics, recent innovations in big data might allow us to estimate fine-grained causal effects, automatically and at scale. But the analytical challenges are substantial.
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Under Armour and Sports Authority link fitness tracking and customer loyalty
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Baltimore Sun
from September 21, 2015
Sports Authority has long been a big distributor of Under Armour gear, but the relationship between retailer and sports apparel brand just got deeper.
In a new agreement, the retailer has linked its loyalty program, The League, to one of the Baltimore-based sports apparel brand’s health and fitness tracking applications, MapMyFitness. The idea is to better understand customers’ habits and offer rewards for meeting fitness goals and healthy living, which in turn allows for targeted marketing and increased sales.
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Volkswagen and the Era of Cheating Software – The New York Times
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The New York Times, The Opinion Pages, Room for Debate
from September 23, 2015
… In a world where more and more objects are run by software, we need to have better ways to catch such cheaters. As the Volkswagen case demonstrates, a smart object can lie and cheat. It can tell when it’s being tested, and it can beat the test.
The good news is that there are well-understood methods to safeguard the integrity of software systems. The bad news is that there is as yet little funding for creating the appropriate regulatory framework for smart objects, or even an understanding of the urgent need for it.
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The Next Generation of Search Engines with Cynthia Rudin, Associate Professor of Statistics at MIT
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Berkman Center, Cynthia Rudin
from September 22, 2015
My goal for this year is to anticipate the next generation of search engines. I say that the current generation of search engines just tells you where to find information (returns a list of webpages), where the next generation of search engines find the information for you (reads the webpages and returns content!). [audio, 1:06:21]
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Events
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Master’s in Data Science Open House
If you would like to learn more about the MSDS program and are in the NYC area, plan to attend our Open House listed below. The Open House provides an overview of the program and gives you a chance to interact with Faculty, Students and Staff of CDS.
Tuesday, September 29, from 6pm – 8pm at 726 Broadway, Rm 701
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Deadlines
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IJCAI-2016 Call for Workshop Proposals
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deadline: subsection?
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The IJCAI-16 Workshop Program Co-Chairs invite proposals for workshops to be held July 9-11, 2016, immediately prior to the main conference. The aim of the workshop program is to provide a structured setting for the discussion of specialized technical topics; the format of proposed workshops should be designed to promote an active exchange of ideas between attendees. All members of the AI community are invited to submit workshop proposals for review.
Submission Deadline: Tuesday, November 3
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