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AI was very popular 30 years ago, then disappeared, and is now making a big come back because of  new robotic technologies: driver-less cars, automated diagnostic, IoT (including vacuum cleaning and other household robots), automated companies with zero employee, soldier robots, and much…

Summary:  It’s official, Data Scientist is the number 1 best job in America.

It’s official, Data Scientist is the number 1 best job in America.  Glassdoor, the site that rates employers and gives job seekers the inside scoop on what to expect just published its 2016 results and Data Scientist is number one.

We got a job score of 4.7…

By Greta Roberts.

It’s exciting to watch advances in predictive and prescriptive employee solutions. A public HR technology company recently announced the release of an application enabling employers to “identify which employee is likely to quit, and what options need to be considered to retain that…

NoSQL also know as not only SQL (structured query language) is a tool used for data design and management of large quantity of distributed data. NoSQL database comprise a wide range of architecture and technologies designed to overcome problems of scalability and big data performance problems.…

I recently shared an article on how airlines are using information to individually price airfares and extract maximum value from passengers which was thought provoking and insightful for many airline industry and big data…

In this blog I will be covering the basic understanding of a data science problem. To begin with I think before starting with any data science problem you should know about 2 key things:

1. What dataset are you going to use?
2. What are the key broad level type of machine learning algorithms?

Let’s look at the each of the…

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the…

In the last post, we talked about how the open sourcing of machine learning algorithms and hardware architecture gives rise to the latest phenomenon of “Data is king”. As companies compete to get developers to use their libraries, we continue to see this ongoing arms race like trend in open sourcing. In addition, we are also seeing how complicated the big data landscape is becoming. This is the link to the most recent …

Originally written by Nigel Higgs on LinkedIn Pulse.

We who have been in the data sphere a while and in and around Data Governance will have seen the pitch-decks, watched the webinars, read the blogs and attended the conferences.  Some of us will have…

One of the hot topics on Machine Learning is, with no doubts, feature engineering. In fact, it comes before the buzz on this topic, simple when we talk about Data Mining. Remembering the CRISP-DM process, feature engineering (and, consequently, feature selection) is the core of a great data mining project – it comes to life on the Data Preparation phase, that is the task to have constructive data preparation operations such as the production of derived attributes or entire new records, or…

Feature selection is one of the core topics in machine learning. In statistical science, it is called variable reduction or selection. Our scientist published a methodology to automate this process and efficiently handle la large number of features (called variables by statisticians). Click here for details.

Here, we mention an article published by Isabelle Guyon…

This is the first in a series about cross format data modeling principles.

Names are Simple, Right?

In the data modeling realm, there is perhaps no example that is as ubiquitous as modelling personal names. After all, things don’t get much simpler than a name:

Simple, right? Well, not so fast. This isn’t really a model, but rather an instance of a model - an actual example that proves out the model. There are, however, a number of ways that…

This article focuses on cases such as Facebook and protein interaction networks. The article was written by By Paul Scherer (paulmorio) and submitted as a research paper to HackCambridge. What makes this article interesting is the fact that it compares five clustering techniques for this type of problems:

• K Clique Percolation - A clique merging algorithm. Given a set kk, the algorithm goes on to produce kk clique clusters and merge…

From querying your data and visualizing it all in one place, to documenting your work and building interactive charts and dashboards, to running machine learning algorithms on top of your data and sharing the results with your team, there are very few limits to what one can do with the Jupyter + Redshift stack. However, setting everything up and resolving all the package dependencies can be a painful experience.

In this blog post I will walk…

Ember Data (a.k.a ember-data or ember.data) is a library for robustly managing model data in Ember.js applications. The developers of Ember Data state that it is designed to be agnostic to the underlying persistence mechanism, so it works just as well with JSON APIs over HTTP as it does with streaming WebSockets or local IndexedDB storage. It provides many of the facilities you’d…

A data-driven organization will use the data as critical evidence to help inform and influence strategy. To be data-driven means cultivating a mindset throughout the business to continually use data and analytics to make fact-based business decisions. Becoming a data-driven organization is no longer a choice, but a necessity. Making decisions based on data-driven approaches not only increases the accuracy of results but also provides consistency in how the results are interpreted and fed…