Featured Blog Posts – March 2014 Archive (38)

Illegal drugs: do they really work?

Since they made marijuana legal in Washington state, I went to one of my favorite restaurants in Seattle, and discovered that they now use marijuana as an ingredient in their famous recipes, in particular in their smoky macarons.

It has absolutely no effect (mind altering) on me, but it also reminded me when I was young and tried a few drugs: none produced anything other than mechanical effect (increased heart beat) on me. Even…


Added by Vincent Granville on March 31, 2014 at 5:00pm — No Comments

MapReduce / Map Reduction Strategies Using C#

A Brief History of Map Reduction

Map and Reduce functions can be traced all the way back to functional programming languages such as Haskell and its Polymorphic Map function known as fmap.  Even before fmap there was the Haskell …


Added by Jake Drew Ph.D. on March 31, 2014 at 6:48am — No Comments

Two new books in my Data Science and Big Data stack

I recently added two new data analytics books from Pearson to my growing Data Science and Big Data stack:

  1. "R for Everyone: Advanced Analytics and Graphics", by Jared Lander.…

Added by Kirk Borne on March 29, 2014 at 11:15am — No Comments

Nate Silver's famous run of successful predictions came to an halt

This is a classic. A guy who correctly predicted election results in all 50 states, and many other correct predictions, now fails.

Nate Silver

First, Nate is well known not because of his previous correct predictions, but because he got hired by the Times magazine where he contributed as a…


Added by Mirko Krivanek on March 29, 2014 at 6:30am — 1 Comment

Is Data Scientist the right career path for you?

According to Paco Nathan, a data scientist should:

  • prepare an analysis and visualization of an unknown data set, while impatient stakeholders watch over your shoulder and ask pointed questions; be prepared to make quantitative arguments about the confidence of the results
  • describe “loss function” and “regularization term” each in 25 words…

Added by Mirko Krivanek on March 28, 2014 at 5:00pm — 11 Comments

From the trenches: 360-degree data science

This is data science from the trenches - both a case study, and a tutorial for data sciencist candidates. Here I illustrate how gut feelings, carefully selected data (rather than getting granular data), full understanding of business (horizontal knowledge), high level vision, and outsourcing (to make data science almost free) combined together, makes a data science project…


Added by Vincent Granville on March 27, 2014 at 10:30am — 2 Comments

Big Data, Big Savings: Environmentalism and High-Tech Collide

Data empowers business: it gives us the information we need to make the decisions that drive enterprises, industries, and economies. Big Data enables us to collect a massive amount of information (that we can store, search, share, and analyze) to bring us closer to the goal of finding trends that lead to smarter business decisions.  Big data has big impact on businesses, governments, and societies, and its impact is continuously growing.  And it gets even bigger than that.

Big Data…


Added by Jon Rabinowitz on March 26, 2014 at 9:48pm — No Comments

The Data Scientist at Work

BRick van der Lans. Originally posted in B-Eye-Network

The Data Scientist’s Four-Step Discovery Process

The discovery process used by data scientists commonly consists of four steps (see also Figure 1):

  • Data…

Added by Vincent Granville on March 26, 2014 at 8:00pm — No Comments

Interesting chart

Published in The Economist. It shows the difference in cost-of-living between 2003 and 2013. However, I see two issues:

  • Making index = 100 for New York both in 2003 and 2013 is wrong. The reader will think New York prices stayed flat over 10 years, and it makes all comparisons 2003-2010 for other cites meaningless, as index might not have evolved the same way outside New York.
  • The choice of cities listed below is questionable. Why is Mexico City not…

Added by Mirko Krivanek on March 26, 2014 at 7:00pm — No Comments

21 Thought-Leader Professors in Data Science

The field of data science continues to grow, and with it come thought leaders who contribute to the industry through outreach and education. Many of the data science professors teaching today are leaders in the big-data field, speaking at conferences, writing books, and even creating groundbreaking big-data developments themselves. Find out which schools boast the most influential leaders in the data science industry.



Added by Vincent Granville on March 26, 2014 at 6:11pm — 3 Comments

Top 10 Capabilities for Exploring Complex Relationships in Data for Scientific Discovery

With all of the discussion about Big Data these days, there is frequest reference to the 3 V’s that represent the top big data challenges: Volume, Velocity, and Variety. These 3 V’s generally refer to the size of the dataset (Volume), the rate at which data is flowing into (or out of) your systems (Velocity), and the complexity (dimensionality) of the data (Variety).  Most practitioners agree that…


Added by Kirk Borne on March 26, 2014 at 4:30am — 1 Comment

The Data Science Toolkit - My Boot Camp Ciriculum

This is a compilation has everything you need to jumpstart your skills in the core tasks of data transformation, modeling, and visualization.

tl;dr: Coursera and John Hopkins have a new course called The Data Scientist's Toolbox. https://www.coursera.org/course/datascitoolbox


Below is a list of popular analysis from Rexer's 2013 survey. The table is biased towards customer transaction, text,…


Added by Peter Higdon on March 25, 2014 at 9:01am — No Comments

The Haboob Clouds Hadoops Future

Hadoop is an open source framework for storing massive amounts of data on clusters of commodity hardware.

Haboob is a dense dust storm that moves…


Added by Michael Walker on March 23, 2014 at 9:03am — 3 Comments

Jackknife logistic and linear regression for clustering and predictions

This article discusses a far more general version of the technique described in our article The best kept secret about regression. Here we adapt our methodology so that it applies to data sets with a more complex structure, in particular with highly correlated independent variables.…


Added by Vincent Granville on March 19, 2014 at 4:00pm — 11 Comments

Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting


This article describes methods for machine learning using bootstrap samples and parallel processing to model very large volumes of data in short periods of time. The R programming language includes many packages for machine learning different types of data. Three of these packages include Support Vector Machines (SVM) [1], Generalized Linear Models (GLM) [2], and Adaptive Boosting (AdaBoost) [3]. While all three packages can be highly accurate for…


Added by Jake Drew Ph.D. on March 19, 2014 at 9:10am — 4 Comments

Learn experimental design with our live, real-time ongoing analysis

This permanent experimental design setting allow you to learn, participate or check out the results at any time, as data is gathered and reported in real time. This article illustrates a few concepts:

  • The necessity to work with redundant data
  • The necessity to identify and use the right metrics
  • How to detect anomalies in experimental design settings
  • How to test multiple factors at once
  • What could make this analysis invalid

You can…


Added by Vincent Granville on March 18, 2014 at 12:00pm — No Comments

Top 10 Business Intelligence Trends for 2014

Tableau webinar, March 25.

Register now.

The innovation in data and analytics continues to…


Added by Vincent Granville on March 17, 2014 at 9:00am — No Comments

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