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
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
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…
[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
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.
This book has been written in layman’s terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, they stick to intuitive explanations and visuals.
In this video, Riku Inoue and Bryan Lares share how a car auction service and a global insurance company were able to adopt TensorFlow and Cloud Machine Learning to solve real-world business problems and improve customer service and product excellence.