Ariella Brown, Technology Blogger, 10/14/2013 Comment now
Handheld devices put a wealth of information at your fingertips, and now museums are using them to enhance visitors' experiences and enable people to relive their experiences afterwards. One thing to remember: that flow of data is a two-way street.
This past summer, when I visited the Museum of Modern Art in New York, instead of audio guides outfitted ...
Big data and analytics are making their mark on education through adaptive learning experiences. But what's good for college isn't necessarily great for fourth grade English.
Education technology companies have been busy in recent years developing programs that automate the education process. There are a few areas where this would appear to be useful.
Ariella Brown, Technology Blogger, 9/6/2013 Comment now
Drawing on the power of big data to enhance learning, Gooru offers teacher and student a curated and sharable playlist of educational materials.
Instead of just repeating the same lesson plans they used in previous years, teachers have a new way to convey the material to their students through Gooru.
John Burke, CIO & Principal Research Analyst, Nemertes Research, 6/10/2013 Comment now
I've spent a lot of time around universities, both during my own graduate and undergraduate years, and in various roles (most recently as chief architect) at various IT departments at several universities. So, I'm fairly confident when I say this: Big data can be a university's best friend... or its worst nightmare.
Rich Heimann, Analytics Engineer, Data Tactics Corporation, 6/6/2013 Comment now
Big social data is driven by a social aspect, and ultimately analyzes data that could serve directly, as or as a proxy, for other more substantive variables. The Flesch-Kincaid index, which you may all be familiar with as a consequence of using Microsoft Word, has for some time provided the readability index to documents.
The best big data insight comes when an organisation looks at itself from the inside out. Approaching the challenge to get your own network as transparent and clean-running as possible is the best grounding for real business intelligence insights.
James M. Connolly, US Correspondent, 5/17/2013 Comment now
College students get queasy when they think of their institution of higher learning as being a business with budgets and management mandates. After all, the classroom, the dorms, and the campus are at the root of the word collegial.
Data Munging is boring but necessary, as it involves getting raw data ready so that the exciting work of analysis can be done.
Let's look at this with an example
Sara's hard at work on data from her local schools, but oh boy, what a mess! Attendance registers are pretty clean, but each school uses different formatting for keeping track of grades and discipline, some keep dates in different formats -- not to mention all the unstructured data sitting there in the form of comments and social media interactions.
Before Sara can get anywhere with this, she needs to do some serious munging, or wrangling. That means getting this information to match up with each other -- so she'll need to extract all of this raw data and run algorithms on it to match up with her preferred columns and rows -- and depositing the finished dataset in her data store so she can start running queries.
OpenStack is a cloud computing project that aims to provide scalable, open-source solutions to businesses. Let's look at how this applies to big data with an example.
An educational collective is looking to get insights on its syllabus. Sara is in charge and has had some early successes. As a result, many other educational institutions have become interested and want to get involved.
Being on OpenStack, Sara's project benefits from being able to scale in a robust fashion (due to the open-source nature), bringing in data sets and computational needs of the added schools. It's a dynamic system, with schools adding and removing data, and the OpenStack allows virtual machines to be created and destroyed as needed.
Hadoop and Cassandra integrate really well with the established OpenStack setup. All this cloud-stored data can be mined and analyzed until the data teams come up with new syllabus hypothesis they can test and adjust -- creating a more seamless year of study for prospective students.
ETL is central to a lot of big data work, standing for Extract, Transform, and Load. But what does that mean? Let's explain it with an example:
Lauren is a data scientist working at a university, looking to bring together different datasets to make sure students are offered courses which best suit their profiles. To do this, she needs to pull data from lots of places into a centralized data warehouse.
First, she needs to extract data from the original sources, which can include existing university databases, as well as web crawling for social media information on students.
Next, Lauren has to transform this extracted data so that it fits in a way the centralized data warehouse can use it. For this, she can use a series of rules or functions to get the data into shape -- for instance, changing DOBs to reflect age, deriving aggregated values, deduplicating records, or joining data from multiple sources, depending on what the final data warehouse needs.
Finally, Lauren can load this data into the data warehouse, giving her a way to gain new insight on students by mining for patterns in this collected data.