Featured Blog Posts – March 2016 Archive (82)

Exploring the Different Roles within Data Science

I recently published a guide to the different career paths within data science, and how different skills and tools can fit together into the perfect data science role. This was part of my research for the comprehensive guide to getting a first data science job I…


Added by Roger Huang on March 31, 2016 at 12:26pm — 1 Comment

Spectral Clustering – How Math is Redefining Decision Making

Guest blog post by Gaurav Agrawal, COO at Soothsayer Analytics.

In today’s world of big data and the internet of things, it is common for a business to find itself sitting atop a mountain of data. Possessing it is one thing, but leveraging it for data driven decision making is a much different ball game. Gut-feelings and institutionalized heuristics have traditionally been used to guide development of…


Added by Vincent Granville on March 31, 2016 at 12:00pm — 1 Comment

Data Art - An Immersive Virtual Reality journey into the world of data science

Forget #dataviz - As immersive #VR emerges we will experience #datarealization which is the experience of touching, smelling, sensing and physically manipulating data in an immersive data art exhibit.

Imagine putting on your #VR headset and you are in a chart. Not a bar or pie chart, but something totally new - an experience chart. You walk to the right and you see mountainous three dimensional structures. As you walk up to one you touch it and you hear a voice say “sales in December… Continue

Added by Eduardo Siman on March 30, 2016 at 6:07pm — No Comments

Predictive Analytics for Unified Communications

More and more organizations today are moving to unified communications (UC) platforms for better communications within their organization, with their customers and with their partners. These platforms combine voice, email, chat and web into a seamless Omni-channel experience for its users. They today boost of a number of features, but most of them provide either static or rule based experiences. Given that these platforms generate tons of data, can this data be used to improve user…


Added by Kumaran Ponnambalam on March 30, 2016 at 4:30pm — No Comments

Weekly Digest, April 4

Starred articles are new additions or updated content, posted between Thursday and Sunday. The weekly digest has six sections: (1) Featured Resources and Technical Contributions, (2) Featured Articles and Case Studies, (3) From our Sponsors, (4) News, Events, Books, Training, Forum Questions, (5) Picture of the Week, and (6) Syndicated Content.

The full version is always published Monday.…


Added by Vincent Granville on March 30, 2016 at 2:00pm — No Comments

New book on data mining and statistics

New book:

Numeric Computation and Statistical Data Analysis on the Java Platform (by S.Chekanov)

710 pages. Springer International Publishing AG. 2016. ISBN 978-3-319-28531-3.  http://www.springer.com/us/book/9783319285290

Book S.V.Chekanov 2016

About this book: Numerical computation, knowledge discovery…


Added by jwork.ORG on March 30, 2016 at 1:35pm — No Comments

To Optimize or to Satisfice When Visualizing Data?

Is there a single “best” way to visualize data in a particular scenario and for a particular audience, or are there multiple “good enough” ways?

That’s the debate that has resurfaced on Stephen Few’s and Cole Nussbaumer’s blogs recently.

In summary, Stephen says, “Is there a…


Added by Ben Jones on March 30, 2016 at 10:04am — No Comments

Data Variety Trumps Volume and Velocity

A new 2016 survey entitled "Big Data Executive Survey 2016" concludes that data variety is the top data priority for most firms. Seasoned data science practitioners have long known that …


Added by Michael Walker on March 30, 2016 at 8:30am — 1 Comment

Ten Signs of Data Science Maturity - Free eBook

Kirk Borne and I recently published "Ten Signs of Data Science Maturity" free O'Reilly ebook (http://www.oreilly.com/data/free/ten-signs-of-data-science-maturity.csp). The ebook identifies the successful characteristics to help build a competency in data science. 

This report provides a detailed discussion of each of the 10 signs of data science maturity, which—among…


Added by Peter Guerra on March 30, 2016 at 3:00am — No Comments

Smart Grid Analytics: Market Trends

Performance Reporting and Real-time Optimization Provided by Smart Grid Analytics to Propel Smart Grid Analytics Market

Smart grid analytics are solutions utilized for analyzing a huge amount of data generated via smart grid systems. Smart grid analytics are employed for gaining an enhanced predictive evaluation of grid conditions and consumer behavior and hence help optimize the efficiency of grids. The prime factor stimulating the growth of the…


Added by Ankit Jain on March 30, 2016 at 3:00am — No Comments

Variance, Clustering, and Density Estimation Revisited


We propose here a simple, robust and scalable technique to perform supervised clustering on numerical data. It can also be used for density estimation, and even to define a concept of variance that is scale-invariant. This is part of our general statistical framework for data science. Previous articles included in this series are:…


Added by Vincent Granville on March 29, 2016 at 9:00pm — 1 Comment

Citizen Data Scientist – Care, Feeding, and Control

Summary:  Management values the self-starting, data-driven, curious, and urgent characteristics that define the Citizen Data Scientist.  But the path to encouraging these individuals also requires setting limits and risk procedures of a wholly new type.  Procedures that will protect the organization so that bad analytic conclusions don’t become bad financial outcomes for the company.



Added by William Vorhies on March 29, 2016 at 8:30am — No Comments

Don't Buy Machine Learning

Author: Marcos Sponton

A few comments for those who are about to invest on Machine Learning intensive project

During a conversation I had with Peter Norvig, we discussed about the kind of projects that we do at Machinalis and how strange does it feels to say that "we are a Machine Learning company": In many projects, the amount of effort spent on R&D on Machine Learning is usually a small fraction of the total effort, or it’s not even there because we plan it for a…


Added by Elías Andrawos on March 29, 2016 at 5:30am — 3 Comments

How to forecast using Regression Analysis in R

Regression is the first technique you’ll learn in most analytics books. It is a very useful and simple form of supervised learning used to predict a quantitative response.

Originally published on Ideatory…


Added by Sudhanshu Ahuja on March 28, 2016 at 8:00pm — No Comments

The Curse of #DataViz

With the wide array of amazing data visualization tools out there - SAP Lumira, Qlik, Domo, Tableau - you would think that the world has moved towards a graphical understanding of reality.

Yet my experience has been that when people are faced with the task of using a fancy new graph or visualization in their work, their first reaction is….to freak out. Now bear with me now. I am not implying that most people can’t handle a nice bar chart or a three…


Added by Eduardo Siman on March 28, 2016 at 2:30pm — No Comments

Getting the Complete Picture from Your Brand’s Social Listening

Social listening has a nasty habit of being completely soft—all about the words without the context of volume and velocity of conversation—or completely quantitative with little information about what’s actually being said. Unfortunately, this leads to conclusions that don’t help you make better strategic decisions. Whichever way your listening leans, you can’t get a clear picture of a situation or your brand online without both the qualitative and the quantitative.




Added by Chris Atwood on March 28, 2016 at 10:30am — No Comments

Lean Analytics: The building blocks

Analytics is still in a phase in many organizations where selling it internally to the stakeholders is the biggest challenge.Creating analytics is a cost- resource intensive investment for enterprises and  evangelizing the trust in data driven thinking and optimizing the opportunity cost due to delay in adoption is the most crucial problem to solve.
All the frameworks which talk about the Analytics mix, various technology & tools stack and maturity roadmaps, are confined to…

Added by Gaurav Kumar on March 28, 2016 at 3:30am — No Comments

Bridging the Gap Between Data Science and DevOps

What’s the real value of data science? Hailed as the sexiest job of the 21st century just a few years ago, there are rumors that it’s not quite proving its worth. Gianmario Spacagna, a data scientist for Barclays bank in London, told Computing magazine at Spark Summit Europe in October 2015 that, in…


Added by Packt Publishing on March 25, 2016 at 2:00am — No Comments

Analytics in Telecom Industry

Telecom industry is one which not only sees a large customer base, but a customer base who’s needs and desires are constantly evolving and/or shifting. On top of this, telecom firms face cut throat competition, making it a highly dynamic and challenging industry. In such a scenario, each decision that a telecom firm takes becomes all the more crucial. It is hence imperative for the firm to take decisions based on extensive data analytics so as to ensure efficient and effective use of…


Added by Tanmay Bhandari on March 24, 2016 at 8:02pm — No Comments

15 Most Controversial Data Science Articles

These articles were controversial in the sense that they highlighted the differences between data science and other disciplines, at a time when many believed that data science was just old stuff being re-branded, or being practiced by people knowing nothing about statistics. Ironically, some of the old stuff actually re-branded itself as data science, not the other way around.…


Added by Vincent Granville on March 24, 2016 at 6:30pm — No Comments

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