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Intel’s Boyd Davis Talks Predictive Analytics and March Madness


“Intel’s goal is to encourage more innovative and creative uses for data as well as to demonstrate how big data and analytics technologies are impacting many facets of our daily lives, including sports. For example, coaches and their staffs are using real-time statistics to adjust games on-the-fly and throughout the season. From intelligent cameras to wearable sensors, a massive amount of data is being produced that, if analyzed in real-time, can provide a significant competitive advantage. Intel is among those making big data technologies more affordable, available, and easier to use for everything from helping develop new scientific discoveries and business models to even gaining the upper hand on good-natured predictions of sporting events.”

Data Science 101: Examining the Requests Made by the Top 100 Sites

File Types Correlation Plot

For our latest installment of the insideBIGDATA Data Science 101 series, I thought I’d do something a bit different. Here is a sample analysis by data scientist and blogger Dan Goldin who published some nice results using R to assess the web requests originating from the top 100 Internet sites.

StatAce for Cloud Statistics with R


StatAce is a start-up in cloud based data science and statistical computing offering an online graphical R environment which allows you to quickly and easily analyze large data. It facilitates collaboration with others, tracks the changes to your R scripts, and can be accessed from any device.

Data Science 101: Deep Learning Methods and Applications


Microsoft Research, the research arm of the software giant, is a hotbed of data science and machine learning research. Microsoft has the resources to hire the best and brightest researchers from around the globe. A recent publication is available for download (PDF): “Deep Learning: Methods and Applications” by Li Deng and Dong Yu, two prominent researchers in the field.

Revolution Analytics and AWS Join Forces


Amazon Web Services (AWS) now offers a hosted version copy of Revolution R Enterprise 7, providing an easy way for individuals and organizations to start and test their big-data-styled analysis projects.

Data Science 101: Building Your Data Science Toolbox

Jeremy Howard made a presentation to the Melbourne R meetup group, where he gave a brief overview of his “data scientist’s toolbox” (using a few Kaggle competitions as practical examples), and also provided an introduction to ensembles of decision trees (including the well-known Random Forest™ algorithm).

Data Science 101: 250 Years of Bayes Theory


It’s been more than 250 years since the appearance of Bayes theorem (named after English statistician, philosopher and Presbyterian minister Thomas Bayes: 1701-1761), one of the two fundamental inferential principles of mathematical statistics.

Learning Data Science in Total Immersion


San Francisco based Zipfian Academy approaches data science education the way some approach learning a new language – total immersion. The company offers a 12-week intensive advanced data science training program in a modern lab environment.

Doing Data Science in the Cloud with Domino


As a practicing data scientist and big data journalist, I often find myself down in the trenches on pursuit of new trends, products, and services. Earlier this week I attended a local machine learning meetup group event and I came away with a real gem. The presenter mentioned in passing a new cloud service called “Domino” and I rushed back to my office to learn more. I wasn’t disappointed.

Becoming a Data Scientist – What Does it Take?

I’ve been monitoring a curious and lively discussion over on LinkedIn – “Is it necessary to have a Masters Degree to become a data scientist?” The comments I’ve seen have exhibited a number of points of view on the matter that I think are reflective of the questions on many people’s minds – both those wanting to become a data scientist and those wanting to hire a data scientist.