FREE eBook: “Analyzing the Analyzers” An Introspective Survey of Data Scientists and Their Work By Harlan Harris, Sean Murphy, Marck Vaisman Publisher: O’Reilly There has been intense excitement in recent Read More
Data Science Report
“Scaling makes your life difficult, Scaling can take hours, days, or weeks to perform which may delay needed data processing”
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.
MLTrain is coming back to New York City for another training event. Nick Vasiloglou and Alex Dimakis will cover several Machine Learning and TensorFlow topics. We have prepared a 2 day curriculum. You can register for each day individually or for both days. The space is offered by Ebay! When: 6/2-6/3 2017, 9:00am to 2:00pm