Python may soon supplant R and assume the mantel of lingua franca for data science computing…For the foreseeable future, Python and R will co-exist as primary languages of data science. Python-averse analysts obsessed with R shouldn’t weep and gnash teeth just yet – but should certainly start down a modern Python learning path if they haven’t already. At the same time, Python programmers from a decade ago must learn a very different environment as they adapt to Python for data science. Modern Python for statistical computation looks very much like R, which, while a challenge for some, is likely a benefit for data science.
By stepping away from binary result sets and augmenting small sample sizes, it’s possible to find players who are perhaps undervalued and overlooked because they’re currently caught in a unlucky series of encounters with firm woodwork, close calls, and exemplary goalkeeping. In the Barclay’s Premier League, where just a few goals can close the thin margin between relegation and survival, finding a few cheap goals can prove to be quite valuable. – See more at: http://www.optasportspro.com/about/optapro-blog/posts/2013/augmenting-free-kick-data-for-more-meaningful-results.aspx#sthash.sPoM9YZH.dpuf
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IBM Big Data platform combined traditional data warehouse technologies with new Big Data techniques, such as Hadoop, stream computing, data exploration, analytics and enterprise integration, to create an integrated solution to address these critical needs.
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The biggest objections to introducing a new structured storage technology are usually related to the experience with the technology within the organization or “single source of record.” While the latter concern is indeed a problem when combining many types of NoSQL databases with an existing SQL database, it wouldn’t be a problem with Neo4j. Like most graph databases, Neo4j is transactional. As for the former consideration, that exists with any new or in this case different technology.
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If you’ve yet to dive into material and begin to understand the impact of what is called the quantified self, we suggest you do. This movement is about to get huge as the sheer volume of those classifying themselves as self-quantifiers is doubling annually. As barriers to quantified self fall – via ever smaller sensors, ever smarter data and ever more passive ways to collect and analyze the data – the continuation of individuals into the greater movement accelerates. If your world is technology and you believe in the consumerization of IT, understand now that quantified self is about to jettison the niche realm and rightfully proclaim the position of next.
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If it is hard to integrate the predictive analytic models that result from your modeling solution into your software product you will spend time and money making your analytics actionable, time and money you could have spent elsewhere. A strong set of APIs, an awareness of how predictive analytics are being used in real-time and good integration tools are critical.
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Riot Games is 100% focused on the gamer experience. They have built the most played video game in the world – League of Legends – and they need to constantly monitor, develop, and update their games to keep gamers engaged. Riot Games is constantly seeking out the most cutting edge data technology to put to work in their business. The company turned to Hadoop to store the massive amount of data they needed to collect because of the scalability it promised. However, they learned early on that it was extremely difficult to extract and explore that data in order to make real time adjustments to the game.
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Google didn’t stop with MapReduce, but they developed other approaches for applications where MapReduce wasn’t a good fit, and I think this is an important message for the whole Big Data landscape. You cannot solve everything with MapReduce. You can make it faster by getting rid of the disks and moving all the data to in-memory, but there are tasks whose inherent structure makes it hard for MapReduce to scale.
Open source projects have picked up on the more recent ideas and papers by Google. For example, ApacheDrill is reimplementing the Dremel framework, while projects like Apache Giraph and Stanford’s GPS are inspired by Pregel.
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