A lot of interesting images can be found on Google. You can search for machine learning cartoons, fake data scientists, Excel maps or any keyword, and get a bunch of interesting images or charts, though the images barely change over time (Google algorithms are very conservative).
Anyway, here's some really interesting stuff. It definitely proves how popular infographics are, and the growth of big data. Many of these infographics are of high quality, well thought out and based on real…Continue
Added by Mirko Krivanek on July 19, 2015 at 7:30am — No Comments
The idea of "big data" could conjure up Turing-like images of massive cloud storage centers humming away in a dessert. It's true that up to this point, computing power has been a barrier to analytics, research, and the exploitation of big data. It takes a lot of computing power to process increasingly complicated and elaborate data sets. Companies, therefore, must…Continue
Added by Yuanjen Chen on July 17, 2015 at 11:30am — No Comments
Our entire family loves going to Olive Garden and every time we go, we order the same items from the menu!
Having access to the Olive Garden nutrition data is like stumbling on a pot of data gold, you never know that sinfully tasty food might be the most nutritious dish, or is it?
Let us start with the appetizers
Added by Nilesh Jethwa on July 16, 2015 at 4:12pm — No Comments
Summary: Flawed data analysis leads to faulty conclusions and bad business outcomes. Beware of these seven types of bias that commonly challenge organizations' ability to make smart decisions.
This is a great article by Lisa Morgan originally published on InformationWeek.com. See the original article…Continue
In this post, I propose that IoT analytics should be a part of 'Smart objects' and discuss the implications of doing so
The term ‘Smart objects’ has been around from the times of Ubiquitous Computing.
However, as we have started building Smart objects, I believe that the meaning and…Continue
IEEE International Conference on Data Mining identified 10 algorithms in 2006 using surveys from past winners and voting. This is a list of those algorithms a short description and related python resources. The detailed paper is…Continue
Added by Pansop on July 16, 2015 at 2:39am — No Comments
The full version is always published Monday. Starred articles are new additions or updated content, posted between Thursday and Sunday. The picture of the week is from the contribution marked with a +, where you will find the details.
Added by Vincent Granville on July 15, 2015 at 12:30pm — No Comments
As the amount of digital information generated by businesses and organizations continues to grow exponentially, a challenge –or as some have put it, a crisis–has developed.
There just aren’t enough people with the required skills to analyze and interpret this information–transforming it from raw numerical (or other) data into actionable insights – the ultimate aim of any Big Data-driven initiative.…Continue
Just a week after a report from research firm Gartner Inc. found that investment in Hadoop-based Big Data…Continue
Added by William Vorhies on July 15, 2015 at 7:38am — No Comments
To get a cohesive view of “data science”, it is useful to trace the origins of tools and techniques used by its current practitioners. These techniques have primarily emerged out of the following four fields:
Microsoft's first acquisition was in 1987 and it has purchased an average of six companies a year.
Microsoft has made eight acquisitions worth over one billion dollars: Skype (2011), aQuantive (2007), Fast Search & Transfer (2008), Navision (2002), Visio Corporation (2000), Yammer (2012), Nokia (2013) and Mojang (2014).
Check out …Continue
Understanding the Role of Analytics in Insurance Industry
The insurance industry is all about assessing risk and managing the same successfully. Life insurance industry operates intrinsically by balancing risk assessment and risk management. Compiled with a large volume of data the insurance industry operates with, arriving at meaningful…Continue
Added by Eddie Soong on July 15, 2015 at 5:53am — No Comments
As a data science professional how would you respond to the question, “How familiar are you with PMML?”
Your choices are:
- “I have no idea what PMML is”
- “I have heard and read about PMML”
- “I have played around with PMML”
- “I have used PMML in my projects”
I have seen many responses to the question and the most popular one was “I have no idea what PMML is”.
Is this surprising? I guess, if we look at the…Continue
Added by Eddie Soong on July 14, 2015 at 11:01am — No Comments
By Venkat Viswanathan and Ravi Ravishankar
As the Internet of Things (IoT) gains momentum, it’s apparent that it will force change in nearly every industry, much like the Internet did. The trend will also cause a fundamental shift in consumer behavior and expectations, as did the Internet. And just like the Internet, the IoT is going to put a lot of companies out of business.…Continue
Added by William Vorhies on July 14, 2015 at 7:30am — No Comments
Clayton Christensen in his brilliant book titled “The Innovator's Dilemma” spoke about disruptive innovation using the following framework:
While established companies in any sector focus on existing customer needs and sustained innovation at the top of the market, they might leave the space open for new competitors to use simple and disruptive…Continue
Added by Debleena Roy on July 14, 2015 at 7:30am — No Comments
DFS, ''the next big thing'' is taking North America by storm and slowly knocking on Europe's doors. The way it works is simple: sports lovers select a team of real world athletes…Continue
Added by Jure Rejec on July 13, 2015 at 6:30am — No Comments
Over the past several years, companies have moved from simply asking where to get the data from to support their decisions, to asking how they are going to leverage it to create actionable insights.
Predictive analytics use statistical or machine-learning techniques to analyze current and historical facts, and find relationships and patterns that can be used to predict…Continue
Added by Sam Button on July 13, 2015 at 6:09am — No Comments
At Machinalis we work daily on projects that fall within the area known today as Data Science. Here are 6 tips and learned lessons for people who want to provide a sustainable data science service and don’t want to avoid the mistakes that we made.
Added by Elías Andrawos on July 13, 2015 at 4:30am — No Comments