
Machine learning is a loose term, it covers many activities. From a software engineering…
This article was posted by Manish Saraswat. In this article, Manish has shared some useful apps (he found) which can improve your necessary data science / analytics skills. These apps can improve your listening skills, logical skills, decision making skills, mathematical skills, statistical skills and much more. They are much more powerful than one could imagine. He has grouped these mobile apps in various categories.
Does your passion lie in Data Science / Analytics…
ContinueAdded by Emmanuelle Rieuf on June 30, 2016 at 10:19am — 1 Comment
These books were added in the last few months. Some do not cost anything (those marked as eBook in the listing below).
Added by Vincent Granville on June 30, 2016 at 8:49am — No Comments
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Added by Michael Walker on June 29, 2016 at 9:00pm — 2 Comments
Original post published to DataScience+
In this post I will show how to collect data from a webpage and to analyze or visualize in R. For this task I will use the rvest
package and will get the data from Wikipedia. I got the idea to write this post from Fisseha Berhane.
I will…
Added by Klodian on June 27, 2016 at 11:48am — No Comments
Original article is posted at Forbes : LINK
People love a good man-versus-machine story. From Deep Blue vs. Kasparov in chess, to Watson vs. Ken Jennings in Jeopardy, there is tremendous fascination in pitting humanity’s best and brightest against ever more powerful technological…
ContinueAdded by Amy Krishnamohan on June 27, 2016 at 10:54am — No Comments
Summary: In this Lesson 2 we continue to provide a complete foundation and broad understanding of the technical issues surrounding an IoT or streaming system so that the reader can make intelligent decisions and ask informed questions when planning their IoT system.
In Lesson 1 |
In This Article… |
Added by William Vorhies on June 27, 2016 at 8:10am — No Comments
By Kostas Pardalis
Marketers have a wealth of data that they access to, containing information that waits to be harnessed. It’s this information that data science promises to turn into actionable knowledge. But how can a marketing professional, better understand this brand new world of data science?
In this post, by going through a simple use case of e-mail marketing…
ContinueAdded by George Psistakis on June 27, 2016 at 4:30am — 1 Comment
What’s the first thing you think of when you hear the phrase “artificial intelligence”? Perhaps it’s the HAL 9000 from 2001: A Space Odyssey, or maybe it’s chess Grandmaster Garry Kasparov losing to IBM’s Deep Blue supercomputer. While those are indeed examples of artificial intelligence, examples of AI in the real world of today are a bit more mundane and a whole lot less sinister.
In fact, many of us use AI, in one form or another, in our everyday lives. The personal assistant on…
ContinueAdded by Max Wegner on June 26, 2016 at 11:04pm — No Comments
Today we discuss how to handle large datasets (big data) with MS Excel. This article is for marketers such as brand builders, marketing officers, business analysts and the like, who want to be hands-on with data, even when it is a lot of data.
If you are not the hammer you are the nail. We, the marketers, should defend our role of strategic decision-makers by staying in control of the data analysis function…
ContinueAdded by Mirio De Rosa on June 26, 2016 at 8:40am — 2 Comments
Complexity, in my point of view, is the key for disruptive evolutions in Data Science and Machine Learning. Approaches as the one from Edgar Morin allow us to see the world through a completely different point of view, analyzing and deconstructing commom sense, leading to a completely new epistemologic view of the world and problems.
Epistemology deals with fundamentals of truth, Socrates, Plato (the cave), Bachelard, Morin, Descartes and so on. An agent-based model is a model where…
ContinueAdded by Rubens Zimbres on June 26, 2016 at 7:01am — No Comments
This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before.…
ContinueAdded by Syed Danish Ali on June 26, 2016 at 5:00am — No Comments
Financial institutions have a regulatory requirement to monitor account activity for anti-money laundering (AML). Regulators take the monitoring and reporting requirements very seriously as evidenced by a recent set of FinCEN fines.
One challenge with AML is that it rarely manifests as the activity of a single person, business, account, or a transaction. Therefore detection requires behavioral pattern analysis of…
ContinueAdded by Arshak Navruzyan on June 26, 2016 at 5:00am — 1 Comment
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.
Featured Resources and Technical Contributions
ContinueAdded by Vincent Granville on June 24, 2016 at 9:30am — No Comments
Have you heard of medical radio-telemetry? Up until very recently, it’s been the closest thing that the medical field has had to remote, cloud-based intel on the human body. But now, with the convergence of IoT, cloud, and big data technologies, the health-care industry is primed for a revolutionary influx of new life-saving results. In this article you’re going to see a plausible scenario from the healthcare industry, but keep in mind that such cloud-enabled IoT advancements are happening…
ContinueAdded by Dhananjay Yadav on June 24, 2016 at 4:00am — No Comments
Anyone who’s attempted to grow or market an online business in the past few years has likely experienced the power of online reviews. Over 67 percent of consumers are influenced by online reviews, and many of them claim that they trust online reviews from strangers about as much as they would trust a personal recommendation from a friend or family member. Not only do these reviews help new…
ContinueAdded by Larry Alton on June 23, 2016 at 10:30pm — No Comments
Finding The Top 100 Analytics Startups was posted by Louis Columbus from Forbes.
To find the top 100 analytics startups, an initial query requesting all companies that develop and sell analytics software to enterprises, competing with business-to-business (B2B) business models was completed using Mattermark data.
Next , advanced query…
ContinueAdded by Emmanuelle Rieuf on June 23, 2016 at 2:00pm — No Comments
This article was posted by Xiu-Shen Wei. Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group.
ContinueAdded by Emmanuelle Rieuf on June 23, 2016 at 11:30am — No Comments
This article was written by Nat Levy from Geekwire. Nat is a staff reporter at Geekwire covering a variety of technology topics, including Microsoft, Amazon, tech startups, and the intersection of technology with real estate, courts and government.
San Francisco Bay Area residents facing expensive housing costs continue to look elsewhere to buy a home, but Seattle has become a less popular landing spot, …
ContinueAdded by Emmanuelle Rieuf on June 23, 2016 at 11:01am — No Comments
Original article is published at Forbes: Link
Ask most people outside academia or Silicon Valley what comes to mind when they hear the term “machine learning” and you’re likely to get a response that involves a movie like “The Matrix” or “Ex Machina.” You’re less likely to hear how it’s a great tool for fraud detection or supply chain…
ContinueAdded by Amy Krishnamohan on June 23, 2016 at 9:44am — 3 Comments
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