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16 Great Blogs Posted in the last 12 Months

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. Below is our third edition.…


Added by Vincent Granville on September 27, 2016 at 8:00am — No Comments

Weekly Digest, September 26

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.


  • As businesses incorporate massive volumes of Internet of Things (IoT) and digital data, they've turned to modern architectures that include Hadoop data lakes for cost-effective storage and…

Added by Vincent Granville on September 24, 2016 at 9:30am — No Comments

Lead your own data science projects with the 3 Ps

There's a lot of literature on learning the technical aspect of data science: statistics, machine learning, data munging, big data. This material will serve you well when starting out or working under a lead. But what about when you are ready to spread your wings and lead a project yourself or embark on a project independently? Here you need a different sort of storytelling - the type that communicates why you are working on a project, what the value is, and what you have accomplished.…


Added by Brian Rowe on September 23, 2016 at 1:30pm — 1 Comment

More data beats better algorithms - By Tyler Schnoebelen


Most academic papers and blogs about machine learning focus on improvements to algorithms and features. At the same time, the widely acknowledged truth is that throwing more training data into the mix beats work on algorithms and features. This post will get down and dirty with algorithms and features vs. training data by looking at a 12-way…


Added by Leena Kamath on September 23, 2016 at 12:00pm — No Comments

Data is not facts - the impossibility of being unbiased

Best intentions

We talk a lot about making decisions based on data but we need to be careful about how hard and fast those decisions are. Our decisions are only as good as our data and our analysis.

Neither can be perfect. Data is always a sample of the full…


Added by Andrew Patricio on September 23, 2016 at 9:00am — No Comments

Real-Time Crime Forecasting Challenge

The Real-Time Crime Forecasting Challenge seeks to harness the advances in ​data science to address the challenges of crime and justice. It encourages data scientists across all scientific disciplines to foster innovation in forecasting methods. The goal is to develop algorithms…


Added by Emmanuelle Rieuf on September 23, 2016 at 8:30am — No Comments

10 Tools for Data Visualizing and Analysis for Business

Digging through messy data and doing numerous calculations just so you can submit a report or arrive at the result of your quarterly business development can sometimes be nigh impossible. After all, we are only human, and by the time we get to the other side of our spreadsheet equation, we have…


Added by Dante Munnis on September 23, 2016 at 3:00am — No Comments

The Next Killer App Waits in Your Data

There are lots of writings about how data analytics changes business and everything, but deep down it only matters how you put these analytics in action. This is a software engineering perspective.

Following the success of AlphaGo, the machine learning software that defeated the Go master Lee Sedol, the Wired magazine released an article titled …


Added by Jari Turkia on September 22, 2016 at 8:30pm — No Comments

Concise Visual Summary of Deep Learning Architectures

This article was written by Fjodor Van Veen. 

With new neural network architectures popping up every now and then, it’s hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first.

So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and…


Added by Emmanuelle Rieuf on September 21, 2016 at 2:30pm — No Comments

How to find out if it's correlation or causation

This article was written by Joseph Rickert. 

We all "know" that correlation does not imply causation, that unmeasured and unknown factors can confound a seemingly obvious inference. But, who has not been tempted by the seductive quality of strong correlations?…


Added by Emmanuelle Rieuf on September 21, 2016 at 2:30pm — No Comments

Beyond the Hype of Big Data

My favorite quote on Big Data is by Dan Ariely who says "Big Data is like teenage sex, everyone talks about it, no one really knows how to do it, everyone things everyone else is doing it, so everyone claims that they are doing it..."

Dan Ariely's views aside, Gartner defines  Big Data as " high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” 



Added by Mark Sharma on September 21, 2016 at 11:00am — 1 Comment

Great articles from top data scientists, posted this week

These articles were all posted in the last few days:


Added by Vincent Granville on September 21, 2016 at 7:30am — No Comments

Beginner's guide to the history of data science

This article was written by Hannah Augur. Hannah is a writer, editor and nerd based in Berlin. She's a researcher with knowledge and work in a variety of fields, including 3D printing, fantasy video games and even coffee manufacturing.

“Big data” and “data science” may be some of the bigger buzzwords this decade, but they aren’t necessarily new concepts. The idea of data science spans many different fields, and has been slowly making its way into the…


Added by Emmanuelle Rieuf on September 20, 2016 at 3:30pm — No Comments

Every Data Science Interview Boiled Down To Five Basic Questions

This article was posted by Roger Huang. Roger Huang heads up growth and marketing at Springboard. He broke into a career in data by analyzing $700 million worth of sales for a major pharmaceutical company. Now he writes content that compiles insights from Springboard's network of data experts to help others do the same.

Data science…


Added by Emmanuelle Rieuf on September 20, 2016 at 10:00am — No Comments

Finding Career Opportunities in AI

Summary:  Are there large, sustainable career opportunities in AI and if so where?  Do they lie in the current technologies of Deep Learning and Reinforcement Learning or should you focus your career on the next wave of AI?


If you’re a data scientist thinking about…


Added by William Vorhies on September 20, 2016 at 7:33am — No Comments

Printed And Flexible Sensors To Be Next Big Point In Internet of Things (IoT)

 Printed electronics are being vouched as the next best thing in Internet of Things (IoT), the technology that is rightly regarded as a boon of advancing technology. Silicon-based sensors are the first that have been associated with IoT technology. These sensors have numerous applications, such as track data from airplane, wind turbines, engines, and medical devices, amongst other internet connected devices.…


Added by Khusro Khan on September 20, 2016 at 12:30am — No Comments

How can organizations successfully convert big data into real-world decisions?

Contributed Article from MIT Professor Devavrat Shah.

According to analyst firm IDC, the amount of data being generated in our digital universe is expected to double every two years, producing more than 40,000…


Added by Vincent Granville on September 19, 2016 at 8:57am — No Comments

Big Data Misconceptions

The idea and concept of “Big Data” has been around for a while now, however it seems there are many people who still believe it is shrouded in mystery. Therefore, in the following post we are going to debunk some of the top big data…


Added by Martin Doyle on September 19, 2016 at 1:30am — No Comments

Smart Cities at the Nexus of Emerging Data Technologies and You


One of the most significant characteristics of the evolving digital age is the convergence of technologies. That includes information management (databases), data…


Added by Kirk Borne on September 18, 2016 at 9:00am — No Comments

Generalized Dynamical Machine Learning

In this year of Rudolf Kalman’s demise, this article is dedicated to his memory.


We introduce a new Machine Learning (ML) solution for Dynamical, Non-linear, In-Stream Analytics. Clearly, such a solution will accommodate Static, Linear and Offline (or any combination thereof) Machine Learning tasks. The value of such a solution is significant because the same…


Added by PG Madhavan on September 18, 2016 at 8:00am — No Comments

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