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How MIT Visualizes Supply Chain Risk | #SNA #predictive

How MIT Visualizes Supply Chain Risk | #SNA #predictive | e-Xploration | Scoop.it
MIT Supply Chain Management Director Bruce Arntzen discusses risk visualization and Sourcemap [Video]

 

How does a company keep tabs on thousands of suppliers? That’s the question Bruce Arntzen tried to answer when he started the Hi-Viz Research Project. As Executive Director of MIT’s Supply Chain Management Program, Arntzen works with corporations to find innovative solutions to supply chain problems. The idea for the Hi-Viz project came during a 2011 meeting of the Supply Chain Risk Leadership Council. A survey of attendees listed Supply Chain Visibility as the top concern. Why? With thousands of suppliers and sub-suppliers, it can be very time-consuming to find the weakest link in a supply chain. Arntzen’s solution: an automatic visualization of the end-to-end supply chain where the weakest links could be seen in real time. Watch his interview to learn how MIT and Sourcemap developed the first automated risk visualization [more details below the fold].

 

In 2015, the Hi-Viz project is partnering with actuarial data providers to provide predictive risk analytics. Sourcemap is making available inventory risk mapping as part of its enterprise software-as-a-service. Want to get involved? Learn more about the Hi-Viz project, or contact Sourcemap for a demo.

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What's (technically) in your tweets? | #datascience #API #twitter

What's (technically) in your tweets? | #datascience #API #twitter | e-Xploration | Scoop.it
Just because you only see 140 characters doesn't mean that Twitter isn't getting complicated behind the scenes. Here's how status objects are evolving.
luiy's insight:

.. an interesting map of what's going on behind your Twitter stream. As it turns out, there is quite a bit of data associated with not just you as a user, but also with every tweet that you post to the service.

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Chorus Project : #Twitter #analytics tool suite | #bigdata

Chorus Project : #Twitter #analytics tool suite | #bigdata | e-Xploration | Scoop.it
Twitter data retrieval and visual analytics. Designed for social research. GUI based for easy access and fast productivity.
luiy's insight:

The Chorus package currently comprises of two distinct programs:

Tweetcatcher

Firstly, we have Chorus-TCD (TweetCatcher Desktop). Tweetcatcher allows users to sift Twitter for relevant data in two distinct ways: either by topical keywords appearing in Twitter conversation widely (i.e. semantically-driven data) or by identifying a network of Twitter users and following their daily ‘Twitter lives’ (i.e. user-driven data).

Tweetvis

Secondly, we have Chorus-TV (TweetVis), which is a visual analytic suite for facilitating both quantitative and qualitative approaches to social media data in social science. Visual analytics (VA) is an interdisciplinary computing methodology combining methods from data mining, information visualization, human-computer interaction and cognitive psychology. The VA approach is highly relevant to the aims of Chorus, enabling exploratory analysis of social media data in an intuitive and user-friendly fashion. Two main views are available within Chorus-TV. The Timeline Explorer (below) provides users an opportunity to analyse Twitter data across time and visualize the unfolding Twitter conversation according to various metrics (including tweet frequency, sentiment, semantic novelty and homogeneity, collocated words, and so on).

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European #MOOCs Scoreboard | #learning #open

European #MOOCs Scoreboard | #learning #open | e-Xploration | Scoop.it

The aim of this scoreboard is to highlight the huge potential that European institutions have in the world of MOOCs and to help visualize this potential by compiling the existing European-provided MOOCs available on different open websites. 


Via Irina Radchenko
luiy's insight:

How we created our MOOC database

 

When we first started preparing to launch Open Education Europa, we attempted to make contact with every higher education institution in Europe, asking to whether or not they offered any MOOCs or other open educational resources. Hundreds of institutions answered us, and with the information they provided we started populating our database. Then we went to the websites of the institutions who had not responded and searched for publicly available MOOCs, which we also added to the database. Finally, we cross-checked with other MOOC providers and aggregators such as iversity and OpenupEd.

 

Updating the scoreboard

 

On an ongoing basis, we monitor for new MOOCs by using Google alerts, RSS feeds, and manual searches. The institutions we have contact with update us when they have new courses. We also check the MOOC providers and aggregators every month to see what’s new. As soon as we find a new MOOC online, we add it to our database. That means that some MOOCs are counted in the scoreboard before the start date of the course.

 

By the way, if you happen to know about a secret stash of MOOCs that aren’t included in our database, please tip us off!

 

 

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#Facial Recognition #Analytics - When #Algorithms Grow Accustomed to Your Face

#Facial Recognition #Analytics - When #Algorithms Grow Accustomed to Your Face | e-Xploration | Scoop.it
Companies are developing software to analyze our fleeting facial expressions and to get at the emotions behind them.

Via AnalyticsInnovations
luiy's insight:

Ever since Darwin, scientists have systematically analyzed facial expressions, finding that many of them are universal. Humans are remarkably consistent in the way their noses wrinkle, say, or their eyebrows move as they experience certain emotions. People can be trained to note tiny changes in facial muscles, learning to distinguish common expressions by studying photographs and video. Now computers can be programmed to make those distinctions, too.

 

Companies in this field include Affectiva, based in Waltham, Mass., and Emotient, based in San Diego. Affectiva used webcams over two and a half years to accumulate and classify about 1.5 billion emotional reactions from people who gave permission to be recorded as they watched streaming video, said Rana el-Kaliouby, the company’s co-founder and chief science officer. These recordings served as a database to create the company’s face-reading software, which it will offer to mobile software developers starting in mid-January.

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Robert McKenzie's curator insight, December 1, 2013 6:08 PM

This is an emerging field and complements some of the post GFC analytics . e.g. people who take less than 3 weeks leave in 1 stint are more likely to have breached policies...add to that facial and voice recognition. A UK university was looking at IR camera's in immigration based upon the hypothesis that 'untruth' caused greater brain activity that could be picked up on an IR camera as a trigger for deeper enquiry. Sentiment++

Ali Anani's curator insight, December 3, 2013 9:33 AM

Information from faces ans how to turn information into knowledge

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#Bayesian Methods for Hackers | #datascience

#Bayesian Methods for Hackers | #datascience | e-Xploration | Scoop.it
Bayesian Methods for Hackers : An intro to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view.
luiy's insight:

Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining.

 

The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe. The official documentation assumes prior knowledge of Bayesian inference and probabilistic programming. We hope this book encourages users at every level to look at PyMC. Secondly, with recent core developments and popularity of the scientific stack in Python, PyMC is likely to become a core component soon enough.

PyMC does have dependencies to run, namely NumPy and (optionally) SciPy. To not limit the user, the examples in this book will rely only on PyMC, NumPy, SciPy and Matplotlib only.

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Leonardo Auslender's curator insight, October 15, 2013 7:40 AM

Not at this moment.

 

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Share Your Google Analytics Data As An Infographic

Share Your Google Analytics Data As An Infographic | e-Xploration | Scoop.it

Wouldn’t it be great to get weekly website performance updates as a simple, easy-to-read graphic?

Now you can go beyond the Google Analytics dashboard with a new creative  – and free – tool by Visual.ly. The New Google Analytics Report automatically delivers an infographic depicting your favorite metrics right to your desktop. See the infographic at the article link for a sample of a full infographic that is generated...


Via Lauren Moss
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Didde Glad's curator insight, March 24, 2013 5:52 PM

Præsentér ledelsesinformation i GRATIS designet dashboard med gnaske få klik 

 

 

 

 

ParadigmGallery's comment, March 25, 2013 11:48 AM
did it, interesting, not so sure the artsy, soft approach to the analytics report is as visually satisfying as the bright, primary colors of google.....
AlGonzalezinfo's curator insight, April 9, 2013 10:03 PM

Awesome scoop, thanks Robin!

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Intuition and Big Data: Value of Human Insight

Intuition and Big Data: Value of Human Insight | e-Xploration | Scoop.it

Some of the best uses of advanced databases or data visualizations is in narrowing down what might be thousands or millions of variables into something that can (be) assessed by a person and then acted on. ~ Stacey Gigginbotham, Gigaom


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blogbrevity's curator insight, January 10, 2013 8:19 PM

Nate Silver, author of "Signal to Noise", predicted there would be a statistical tool to forecast baseball using the Pitch f/x data somewhere in the future.  My son built this tool three years ago ... when he was 19.

 

 

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Les facteurs de réussite d’un réseau social d’entreprise | #CI #analytics #RSE

Les facteurs de réussite d’un réseau social d’entreprise | #CI #analytics #RSE | e-Xploration | Scoop.it
Les performances des réseaux internes d'une vingtaine d'entreprise ont été comparées par le cabinet de conseil Lecko pour déterminer ce qui les mène au succès... ou pas.

Via Eric Laurent
luiy's insight:

Beaucoup de sociétés ont des réseaux sociaux d’entreprise (RSE), mais toutes ne rencontrent pas le même succès avec ces projets. Pour tenter de déterminer les facteurs de réussite de ces espaces collaboratifs, le cabinet de conseil Lecko a réalisé un benchmark pour la deuxième année consécutive. Une vingtaine de grandes entreprises ont été comparées via l’outil Lecko RSE Analytics qui renvoie des métriques sur l’activité sociale enregistrée sur les plateformes (création d’un profil, ajout d’un commentaire ou « like »). Pour compléter le tout, plus de 90 community manager ont été interrogés pour comparer leurs pratiques. L’importance des community manager ne se dément pas. 71 % des espaces performants sont nés de l’initiative d’un community manager (voir le tome 7 de l'étude sur l'Etat de l'art des réseaux sociaux d'entreprise de Lecko)

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#Taxonomy of Data Scientists | #datascience #skills

#Taxonomy of Data Scientists | #datascience #skills | e-Xploration | Scoop.it
This is a first attempt at classifying data scientists. I invite you to produce a more comprehensive, better solution.

The 10 pioneering data scientists liste…
luiy's insight:

Who is the purest data scientist?

 

Vincent Granville compares the 4-skill mix of each of these 10 data scientists (as found in the above table), with the generic data science skill mix identified in the previous article (Data Science = 0.24 * Data Mining + 0.15 * Machine Learning + 0.14 * Analytics + 0.11 * Big Data). In short, I computed 10 correlations (one per data scientist) to determine who best represents data science.

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The Impact Cycle – how to think of actionable insights | #datascience #methods

The Impact Cycle – how to think of actionable insights | #datascience #methods | e-Xploration | Scoop.it
luiy's insight:

I. Identify the question. In a non intrusive way, help your business partner identify the critical business question(s) he or she needs help in answering. Then set a clear expectation of the time and the work involved to get an answer.

 

M. Master the data.This is the analyst’s sweet spot—assemble, analyze, and synthesize all available information that will help in answering the critical business question. Create simple and clear visual presentations (charts, graphs, tables, interactive data environments, and so on) of that data that are easy to comprehend.

 

P. Provide the meaning. Articulate clear and concise interpretations of the data and visuals in the context of the critical business questions that were identified.

 

A. Actionable recommendations. Provide thoughtful business recommendations based on your interpretation of the data. Even if they are off-base, it’s easier to react to a suggestion that to generate one. Where possible, tie a rough dollar figure to any revenue improvements or cost savings associated with your recommendations.

 

C. Communicate insights. Focus on a multi-pronged communication strategy that will get your insights as far and as wide into the organization as possible. Maybe it’s in the form of an interactive tool others can use, a recorded WebEx of your insights, a lunch and learn, or even just a thoughtful executive memo that can be passed around.

 

T. Track outcomes. Set up a way to track the impact of your insights. Make sure there is future follow-up with your business partners on the outcome of any actions. What was done, what was the impact, and what are the new critical questions that need your help as a result?

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#Measuring the #Intelligence of #Crowds | #CI

#Measuring the #Intelligence of #Crowds | #CI | e-Xploration | Scoop.it
Microsoft Research in the UK reports useful results on crowdsourcing, based on aggregating questions from a standard IQ test on Amazon’s Mechanical Turk (AMT).
luiy's insight:

The Abstract for their 2012 study Crowd IQ: Measuring the Intelligence of Crowdsourcing Platforms describes the research and findings succinctly:

 

We measure crowdsourcing performance based on a standard IQ questionnaire, and examine Amazon’s Mechanical Turk (AMT) performance under different conditions. These include variations of the payment amount offered, the way incorrect responses affect workers’ reputations, threshold reputation scores of participating AMT workers, and the number of workers per task. We show that crowds composed of workers of high reputation achieve higher performance than low reputation crowds, and the effect of the amount of payment is non-monotone—both paying too much and too little affects performance. Furthermore, higher performance is achieved when the task is designed such that incorrect responses can decrease workers’ reputation scores. Using majority vote to aggregate multiple responses to the same task can significantly improve performance, which can be further boosted by dynamically allocating workers to tasks in order to break ties.

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Collaborative Analytics: #Analytics for your #BigData via @YvesMulkers

Collaborative Analytics: #Analytics for your #BigData via @YvesMulkers | e-Xploration | Scoop.it
So what is collaborative analytics™ and why should one care about it? If you read my blog on “Rethinking Big Analytics to handle BigData”, you kind-of get the gist on the need for some better analy...
luiy's insight:

Our ways to analyze the data is still old school. They are either human or machine dependent and are very isolative with collaborations only possible via manual ways. We all know that there is always someone around you who knows more that you do and could help you get to the next step. The problem is that manual ways are not efficient in helping find those people. So, what should one do?- Invent ways to make analytics discoverable, to make best practices flow and that too with minimal impact to business. A good start will be to start digging for areas that will make our analytics strategy robust and scalable with evolving technological landscape and changing customer dynamics. And if possible, keeping the strategy least invasive and in line with current business practice. No, getting whole minority report on your current business will take forever, and is not very cost effective, you need something sustainable, something that could be implemented in small scale and easy to replicate. That would make collaborative analytics possible.

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Desire2Learn’s New Learning Suite Aims To Predict Success, Change How Students Navigate Their Academic Career

Desire2Learn’s New Learning Suite Aims To Predict Success, Change How Students Navigate Their Academic Career | e-Xploration | Scoop.it

To do this, Desire2Learn wants to bring predictive analytics into play in education. But why? Well, first and foremost because, today, if students want to figure out whether a course is right for them — or how well they might perform in that course — they’re hard pressed to find a good answer. They can ask fellow students, check websites that rank faculty based on nebulous criteria or try to find surveys, but none of these options are ideal.

 

With its new analytics engine, Desire2Learn aims to change that by giving students the ability to predict their success in a particular course based on what they’ve studied in the past and how they performed in those classes. The new, so-called “Student Success System,” was built (in part) from the technology it acquired from Degree Compass; however, while Degree Compass used predictive analytics to help students optimize their course selection, the new product aims to help both sides of the learning equation: Students and teachers.

 

On the teacher side, Desire2Learn’s new analytics engine allows them to view predictive data visualizations that compare student performance against their peers so that they can identify at-risk students, for example, and monitor a student’s progress over time.

 

The idea is to give teachers access to important insight on stuff like class dynamics and learning trends, which they can then combine with assessment data, to improve their instruction or adapt to the way individual students learn. In theory, this leads not only to higher engagement, but also better outcomes


Via Huey O'Brien
luiy's insight:

Essentially, the tool allows students to move their academic resume to the cloud so they can take it with them after they graduate, which the company is incentivizing by offering 2GB of free storage.

 

Basically, what we’ve come to realize, the Desire2Learn CEO tells me, is that the company’s initial approach to business (or academic) intelligence was off track. “Students and teachers don’t necessarily want more data, they want more insight and they want that data broken out in a way that they can understand and helps them more quickly visualize the learning map,” he says.

 

When I asked if building and adding more and more tools and features would dilute the experience and result in feature overload, Baker said that the company doesn’t want to build a million different tools. Instead, it wants to become a platform that supports a million tools and allows third-parties that specialize in particular areas of education to help develop better products.

 

Through open-sourcing its APIs, Desire2Learn along with Edmodo and an increasing number of education startups are beginning to tap into the potential inherent to the creation of a real ecosystem. Adding predictive analytics tools gives Desire2Learn another carrot with which they hope to be able to draw both teachers, students and development partners into its ecosystem.

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Healthcare IT Pros Can Do Great Things with a Strong Infusion of Big Data | Attunity Blog

Healthcare IT Pros Can Do Great Things with a Strong Infusion of Big Data | Attunity Blog | e-Xploration | Scoop.it
Healthcare IT Pros Can Do Great Things with a Strong Infusion of Big Data (RT @attunity: Healthcare IT Pros Can Do Great Things w/ a Strong Infusion of #BigData http://t.co/9coxSZ2AbO [BLOG] #healthIT...
luiy's insight:

The potential for transformation-based big data analytics has become clear in nearly every field over the past few months. Modern organizations simply deal with more data on a regular basis than they did in recent years, meaning the resources needed to complete the transition are in place. This generated an interesting tension in sectors like healthcare. Professionals in these fields can do great things - even save lives - with a strong infusion of big data. However, they must make sure they have powerful ways to transfer the information they use. Without dedicated options like enterprise file replication, these firms could end up simply throwing away investment money.

 
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