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.
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).
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.
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.
The video below comes to us from the Strata Conference 2014: How Companies are Using Spark, and Where the Edge in Big Data Will Be. While the first big data systems made a new class of applications possible, organizations must now compete on the speed and sophistication with which they can draw value from data. […]
H2O, the open source in-memory machine learning and predictive analytics company for big data, announced a partnership with Cloudera, a leader in enterprise data management powered by Apache™ Hadoop.