For those of you who wish to begin learning Python for Data Science, here is a list of various resources that will get you up and running. Included are things like online tutorials and short interactive course, MOOCs, newsletters, books, useful tools and more.…

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week. 

Announcement

• eMetrics Summit New…

Even today there seems to be considerable debate over the exact meaning of "stress." During my graduate studies, I pointed out that there are similar themes in the literature pertaining to stress; that perhaps researchers have been overemphasizing the differences. Among the most persistent themes is the distinction between stress and stressors. Hans Selye asserted in 1936 is that stress is an internal response against any form of noxious stimulant. He described the environmental factors…

The Brussels data science community is pleased to announce the first data science bootcamp organized in Europe aimed at young potentials.

After 75 meetups, 50 datascience training days, many data4good hackathons and already 2 successful DISummits with over 500 participants we sat down with our innovation partners and compiled the…

This notebook was written by Dr. Randal S. Olson from GitHub. In this notebook, Randal is going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like. In addition to providing code examples, he also hopes to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist. Randal will be following along with the data analysis checklist from …

Originally posted here, where you can see all the graphics. 

There has been much in the news lately about the next wave of MT technology driven by a technology called deep learning and neural nets (DNN). I will attempt to provide a brief layman’s overview about what this is, even though I am barely qualified to do this (but if Trump can run for POTUS then…

Under growing pressure to report accurate findings as they interpret increasingly larger amounts of data, researchers are finding it more important than ever to follow sound statistical practices. 

For that reason, a team of statisticians including Carnegie Mellon University's Robert E. Kass wrote "Ten Simple Rules for Effective Statistical Practice." Published in PLOS…

Here are some white papers about Tamr, Lavastorm, Teradata, Rapidminer, Looker, Thingworx, and DataRobot :

Tamr

• Three Problems that Sabotage Analytics and How to Fix Them - Three Problems that Sabotage Analytics Every Time — and Four Ways to Solve Them. A recent Forbes survey of large global…

Going somewhere nice for your summer holidays? Somewhere with a nice beach perhaps – Goa, Grand Cayman or Grimsby? Or a bustling city break? Wherever you’re going there’s sure to be long periods where you’ll sit for hours on end with little to do but read, so I thought I’d throw together a few free eBooks for your Kindle to while away the long hours in the airport, in a traffic jam or on the beach.

A mixture of books about data, analysis, statistics and R programming, they’re all very…

Visual Analytics and Data Discovery allow analysis of big data sets to find insights and valuable information. This is much more than just classical Business Intelligence (BI). See this article for more details and motivation: "Using Visual Analytics to Make Better Decisions: the Death Pill Example". Let's take a look at important characteristics to choose the right tool for…

Written by sought-after speaker, designer, and researcher Stephanie D. H. Evergreen, Effective Data Visualizationshows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints—supported by research and the author’s extensive experience with clients in industries all over the world—for conveying data in an impactful way. Delivered in Evergreen’s humorous and approachable style, the book covers the…

The FlyElephant team is happy to announce the release of the platform FlyElephant 2.0, with…

Here's our selection for today. We will continue to post articles from highly respected data scientists in the coming weeks.

• 7 Traps to Avoid Being Fooled by Statistical Randomness - By Kirk Borne
• How You Can Improve Customer Experience With Fast Data…

Reading some recent blogs, I sense a level of angst among Data Science practitioners about the nature of their field. What exactly IS Data Science - a question that seems to lurk just below the surface . . .

As a young field of study and work, it will naturally take time for a definition of Data Science to crystallize. In the meantime, see if this works for you . . .…

Summary:  To ensure quality in your data science group, make sure you’re enforcing a standard methodology.  This includes not only traditional data analytic projects but also our most advanced recommenders, text, image, and language processing, deep learning, and AI projects.

A Little History…

Overview

There are huge numbers of variants of deep architectures as it’s a fast developing field and so it helps to mention other leading algorithms. The list is intended to be comprehensive but not exhaustive since so many algorithms are being developed [1] [2][1],[2].

1. Deep High-order Neural Network with Structured Output (HNNSO).
2. Deep convex network.
3. Spectral networks
4. noBackTrack algorithm to solve the…

Text classification (a.k.a. text categorization) is one of the most prominent application of Machine Learning. The purpose of text classification is to give conceptual organization to large collection of documents.An interesting application of text classification is to categorize research papers by most suitable conferences. Finding and selecting a suitable academic conference has always been a challenging task especially for…

With marketing and advertising gaining more space on the internet, big data analytics are playing a prominent role in following the trends on the market and providing users with key statistics.

Data analysis and statistics traditionally play an important role in…

Many thanks for the retweets and feedback on Part one of this blog 

A methodology for solving problems with DataScience for Internet of Things - Part One

Here is Part Two

Here we extend the discussion and also suggest a practical (and open) way to create a way forward

To recap, lets keep in mind the…