Posts Tagged ‘data science’

Word To Developers: Power Your Apps With Data And Insights

aplogoApigee wants the development community to be able to seamlessly take advantage of predictive analytics in their applications.

“One of the biggest things we want to ensure is that the development community gets comfortable with powering their apps with data and insights,” says Anant Jhingran, Apigee’s VP of products and formerly CTO for IBM’s information management and data division. “That is the next wave that we see.”

Apigee wants to help them ride that wave, enabling their business to better deal with customer issues, from engagement to churn, in more personal and contextual ways. “We are in the business of helping customers take their digital assets and expose them through clean APIs so that these APIs can power the next generation of applications,” says Jhingran. But in thinking through that core business, “we realized the interactions happening through the APIs represent very powerful signals. Those signals, when combined with other contextual data that may be in the enterprise, enable some very deep insights into what is really happening in these channels.”

With today’s announcement of a new version of its Apigee Insights big data platform, all those signals generated – through API and web channels, call centers, and more – can come together in the service of predictive analytics for developers to leverage.

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RoadMap Your Text Analytics Initiative

analyticspixWhat best practices should inform your company’s text analytics initiatives? Executive Lessons on Modern Text Analytics, a new white paper prepared by: Geoff Whiting, principal at GWhiting.com and Alesia Siuchykava, project director at Data Driven Business provides some insight. Contributors to the lessons shared in the report include Ramkumar Ravichandran, Director, Analytics, at Visa and Matthew P.T. Ruttley, Manager of Data Science at Mozilla Corp

One of the interesting points made in the paper is that text analytics can be applied to many use cases: customer satisfaction and management effectiveness, product design insights, and enhancing predictive data modeling as well as other data processes. But at the same time, a takeaway is that it is better for text analytics teams to follow a narrow path than to try to accommodate a wide-ranging deployment. “All big data initiatives, and especially initial text analytics, need a specific strategy,” the writers note, preferable focusing on “low-hanging fruit through simple business problems and use cases where text analytics can provide a small but fast ROI.

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GraphLab Create Aims To Be The Complete Package For Data Scientists

glabData scientists can add another tool to their toolset today: GraphLab has launched GraphLab Create 1.0, which bundles up everything starting from tools for data cleaning and engineering through to state-of-the-art machine learning and predictive analytics capabilities.

Think of it, company execs say, as the single platform that data scientists or engineers can leverage to unleash their creativity in building new data products, enabling them to write code at scale on their own laptops. The driving concept behind the solution, they say, is to make large-scale machine learning and predictive analytics easy enough that companies won’t have to hire huge teams of data scientists and engineers and build the big hardware infrastructures that lie behind many of today’s Big Data-intensive products. And, the data scientists and engineers that do use it won’t need to be experts at machine-learning algorithms – just experienced enough to write Python code.

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Many More Developers Needed for IoT

Photo of young person learning computing.Matt Asay of ReadWrite reported, “It’s standard to size a market by the number of widgets sold, but in the Internet of Things, which numbers sensors and devices in the billions, widget counts don’t really matter. In part this is because the real money in IoT is not in the ‘things,’ but rather in the Internet-enabled services that stitch them together. More to the point, it’s because the size of the IoT market fundamentally depends on the number of developers creating value in it. While today there are just 300,000 developers contributing to the IoT, a new report from VisionMobile projects a whopping 4.5 million developers by 2020, reflecting a 57% compound annual growth rate and a massive market opportunity.”

The author continued with, “In the last 30 years we’ve created a fair amount of data, but it pales compared to what we’ve generated just in the last two years. Ninety percent of the world’s data was generated in the last two years alone, much of it by machines. Such machine-produced data dwarfs human-generated data. In such an IoT world, devices are not the problem. According to Gartner, we’ll have 26 billion of them by 2020. Connecting them isn’t, either. As VisionMobile’s report makes clear, however, ‘making sense of data’ is the real challenge. It’s also the big opportunity…”

 

Read more here.

 

Photo courtesy flickr / kelliamanda

Where The Money Is: Data Science

datascigrafWhat’s a data scientist worth? Give me a second to identify the relevant data sources, build the machine learning algorithm and create a visualization.

So much for a new take on an old joke. The real answer is about six figures, information I recently came across in a report released earlier this spring: Burtch Works Executive Recruiting survey, Salaries of Data Scientists. The median base salary of data scientist managers is $160,000, it says, while individual contributors average about $120,000. The information comes from 171 data scientists for whom the recruiting firm has complete and current information. Whether a data scientist is at a lower or higher job level, across the board he or she is doing financially better than other Big Data professionals, the report shows.

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Reason To Be Thankful: Being Named A Fast-Growing Tech Company

rsz_thankful_imageIt’s got to be a happy Thanksgiving for a number of tech companies that made their way to Deloitte’s recently-released Technology Fast 500. The 2013 ranking of the fastest-growing tech companies based in North America also has something to show for anyone who’s doubted that there’s money to be made taking advantage of semantic and other Web 3.0 concepts, a look at the list should show it’s time for the doubting to stop.

Have a look at some of the winners with their overall rankings:

#2 Acquia. Drupal claims the title of being the first mainstream content management system to support semantic web technology in its core. The Drupal-powered project Acquia was co-founded by Drupal creator Dries Buytaert to provide cloud, SaaS, and other services to organizations building websites on Drupal – and has on staff software engineer Stéphane Corlosquet, who had a big hand in bringing those semantic capabilities to Drupal’s core. In fact, Corlosquet spoke at the most recent SemTechBiz about Acquia as an example of a Drupal-powered project managing its content as Linked Data.

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Data Scientists Now Can Get A Second Opinion On Machine Learning Problems

rsz_opinion2pixEarlier this month The Semantic Web Blog provided a look at Skytree’s machine learning-as-a-platform approach (see story here). This week, the vendor launched in limited beta a program to help businesses partner up their data scientists and analytics professionals with its software and data scientists. It’s dubbed Second Opinion, and The Semantic Web Blog conducted an email interview with co-founder and CEO Martin Hack about the new effort.

Semantic Web Blog: Is this service available only to existing customers?

Hack: Skytree Second Opinion is for both existing and new customers, essentially everyone  interested in gaining firsthand experience with the power of machine learning, and how advanced analytics can give them greater insight into their data.

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3 Transformations of IT

David Hill of Network Computing recently shared his theory on the three transformations of IT. He writes, “The first was the digitization of business. The second is the continuing digitization of human experience. The third stage is the digitization of machines. Each transformation is ongoing, builds upon the others, and may overlap. Thus, some technologies that formed a foundation earlier are still active. For example, the mainframe is still alive and well, even in the time of mobile computing. Even though specific technologies provide a frame of reference, these transformations span a broad perspective and are not dependent upon any one technology. Please also note that there is not a smooth transition to each transformation, but that elements of a later transformation may be present while the key transformation of an earlier era is still more prominent.” Read more

Semantic Tech: It’s Moving Mainstream, Playing To The Data-Is-An-Asset Crowd, And Living Life Out Loud

At the recent SemTech conference in NYC, The Semantic Web Blog had an opportunity to ask some leaders in the field about where semantic technology has been, and where it’s going.

David Wood, CTO, 3RoundStones:

The short take: Hiring has been on in a big way at semantic tech players as enterprises are moving in greater numbers to buy semantic software, recognizing their traditional vendors won’t solve their interoperability issues. Sem tech vendors should have a happy 2013 as semantics continues going mainstream.

The full take:

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Free Online Resources: Bone Up on Your Data Science and Machine Learning

The Conductrics blog has shared a list of data science and machine learning resources. The introduction states, “Every now and then I get asked for some help or for some pointers on a machine learning/data science topic.  I tend respond with links to resources by folks that I consider to be experts in the topic area.   Over time my list has gotten a little larger so I decided to put it all together in a blog post. Since it is based mostly on the questions I have received, it is by no means complete, or even close to a complete list, but hopefully it will be of some use.  Perhaps I will keep it updated, or even better yet, feel free to comment with anything you think might be of help. Also, when I think of data science, I tend to focus on Machine Learning rather than the hardware or coding aspects. If you are looking for stuff on Hadoop, or R, or Python, sorry, there really isn’t anything here.” Read more

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