FLINK FORWARD Berlin, from September. Keynote from Xiaowei Jiang, Senior Director at Alibaba. “Bring Flink to Large Scale Production”
So what is the difference? “Data Science vs Machine Learning” “A simple distinction between Data Science vs Machine Learning for project managers and team leads.” by Charles Martin, PhD Data Science & Machine Learning Expert (YouTube Channel) OTHER: Artificial Intelligence Vs. Machine Learning Vs Data Science Vs Deep Learning My Quora Answer to: What […]
[Continually Updated] MastersInDataScience.org has culled together a list of 23 of the best US Universities/Colleges that offer a Masters in Data Science. Read More
A FREE Masters in Data Science. More and more people are learning on-line via the flood of excellent “open source” resources of classes, ebooks, software, etc. Clare Corthell has created a website to allow anybody to take virtually the same curriculum offered for a Masters in Data Science for Free.
Will it be an official Masters? No, but an official Masters is not always what is needed. Often its the knowledge and experience working with the tools and techniques necessary to actually do Data Science. For some, this free curriculum will allow business-line leaders, Analysts and Programmers from other fields to fill in the education gaps and get better at their job, as well as, one step closer to being an actual Data Scientist. Read More
From PyData Amsterdam, April 2017. Giovanni Lanzani gives a talk on the Data Science Process and where things can go wrong.
This article is several years old, but it’s still very relevant today. I get asked this question all the time by students. Hope you find it helpful. – Ted
(Refreshed Post) Predictive Analytics Made Easy!
Download this free eBook now and see how you can start using Predictive Analytics today to drive business decisions! Read More
Domino Data Labs recently made the Gartner 2017 Magic Quadrant for Data Science Platforms. Here, Domino’s Chief Data Scientist, Eduardo Arino de la Rubia, does a great job making the very complex easily understandable. Learn more about the platform…
This million dollar Kaggle contest is structured into two rounds. In the qualifying round, opening today, you’ll be building a model to improve the Zestimate residual error. The top 100 ranking teams in this round will advance to the final round.
Martin Heller, Contributing Editor, InfoWorld (2017) reviews half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and TensorFlow.