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Marketers are all over big data - but are they looking to big data at the expense of true insights and missing the heartbeat of their customers?
Today’s connected consumer has access to an insane amount of information, all at their fingertips, thanks to the ubiquity of smartphone access to the web.
From checking restaurant reviews and stock prices, to taking pictures of a new pair of jeans and asking the opinion of friends on Facebook, today’s consumer is no longer restricted to choosing a brand through a push marketing approach.
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Same can be said for learning as well, we track a lot in LMS's but are we measuring what matters? Have we defined, and agreed upon, the metrics that will help the business and the performance of the community we serve?
Mode, a company that calls itself the "Github for Data Analysis" and will be an online repository for data science work.
Germany crushing Brazil in their huge semifinal match of the World Cup was predictable, if you believe the science. It's apparent that Die Mannschaft took a typically German approach--and it worked...
Those of us working in industry with Excel are familiar with scatter plots, line graphs, bar charts, pie charts and maybe a couple of other graph types. Some of us have occasionally used the Analysis Pack to create histograms that don’t update when our data changes (though there is a way to make dynamic histograms in Excel; perhaps I’ll cover this in another blog post).
dat is an open source tool that enables the sharing of large datasets, the goal being a collaboration flow similar to what git offers for source code.
Companies face an inherent tension between being open or proprietary, but we’ve seen, again and again, that open systems can act as catalysts for entirely new businesses built on top of a popular platform.
Scientific Data is a new open-access, online-only publication for descriptions of scientifically valuable datasets.
The increasing availability of big data from mobile phones and location-based apps has triggered a revolution in the understanding of human mobility patterns. This data shows the ebb and flow of the daily commute in and out of cities, the pattern of travel around the world and even how disease can spread through cities via their transport systems.
So there is considerable interest in looking more closely at human mobility patterns to see just how well it can be predicted and how these predictions might be used in everything from disease control and city planning to traffic forecasting and location-based advertising.
Today we get an insight into the kind of detailed that is possible thanks to the work of Zimo Yang at Microsoft research in Beijing and a few pals. These guys start with the hypothesis that people who live in a city have a pattern of mobility that is significantly different from those who are merely visiting. By dividing travellers into locals and non-locals, their ability to predict where people are likely to visit dramatically improves.
The question that Zimo and co want to answer is the following: given a particular user and their current location, where are they most likely to visit in the near future? In practice, that means analysing the user’s data, such as their hometown and the locations recently visited, and coming up with a list of other locations that they are likely to visit based on the type of people who visited these locations in the past.
Zimo and co used their training dataset to learn the mobility pattern of locals and non-locals and the popularity of the locations they visited. The team then applied this to the test dataset to see whether their algorithm was able to predict where locals and non-locals were likely to visit.
They found that their best results came from analysing the pattern of behaviour of a particular individual and estimating the extent to which this person behaves like a local. That produced a weighting called the indigenization coefficient that the researchers could then use to determine the mobility patterns this person was likely to follow in future.
Over the past few years, there has been an explosion of applications, particularly in mobile. Everything from instant stock quotes, to weather forecasts and GPS navigation are now available with a touch of your finger on your cell phone. Surgeons can scroll through years of patient history in minutes. Smart meters bring instant feedback to consumers, reducing energy waste and saving money, without any sacrifice to lifestyle.
Creating a P2P client based server is easy using the WebRTC data channel. Read how 2 Stanford students crafted how to make an "on the fly" server.
Researchers at the University of Michigan have developed a mobile app that monitors subtle qualities of a person’s voice during everyday phone conversations that shows promise for detecting early signs of mood changes in people with bipolar disorder.
Where are bunch of R-related data from a variety of different sources to create some interactive cartograms to highlight the focus of R activity from various points of view.
Web Observatory wiki
Imagine that your local ambulance and fire dispatch runs on an algorithm developed with the help of the data scientists who built Lyft’s grid-optimization system.
This year for State of the Map we look at the last eight years of OpenStreetMap's growth and change.
Founded in 2004, OpenStreetMap has grown from a local project to a worldwide map with widespread use and high data quality.
ZENODO builds and operate a simple and innovative service that enables researchers, scientists, EU projects and institutions to share and showcase multidisciplinary research results (data and publications) that are not part of the existing institutional or subject-based repositories of the research communities.
Spark is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.
System integration with higher education applications can be a challenge. There are many different vendors, agencies and internal systems that need to connect with an institution’s enterprise data. The myriad of applications and ERP systems can make management of multiple direct “one-off” integrations nearly impossible. To simplify this process, IData has brought their years of higher education integration services and their innovative product development skills together to create the IDataHub. The IDataHub is designed to be an integration middleware layer between the higher education ERP systems and the universe of third-party data consumers and providers.
Businesses Need Data Scraping Service to understand the your customers and their preferences. Website scraping experts assists to find new data, trends, and data to analyze and evaluate your businesses.
An interactive exploration of ridership, congestion, and delay on Boston's subway system.
Big data promises automated actionable knowledge creation and predictive models for use by both humans and computers.
The Big Data open source tools landscape is growing rapidly. We have just released edition 2.0
It's time to learn up about the data revolution, and begin to understand your data rights.
D3 based reusable chart library