Introduction:

Here’s a fun fact; An average human being (probably an adult) makes close to 30,000 conscious decisions every day. This isn’t entirely true though, in fact, I just made that number up. I could be right because if you think about it, how many decisions would you say you make on a day to day basis? Depending on who you are the above obviously varies widely and you know best. We all…

Summary:  A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.

A little reminiscence, or for those of you who are only recently data scientists, a little history lesson. 

Our profession of…

Introduction

Big data is the buzzword today, isn’t it?

We live in the golden age of what…

According to a report conducted recently, it has been found that around 600 million people all around the globe use mobile devices. It also has been revealed that the users mostly use the mobile devices not just for making calls but also for using different mobile applications. The latest advancements of these devices alAlow for more engaging, complex and user-friendly applications. Because of this rapidly increasing prevalence among the universal population, mobile devices have become an…

Cluster.OBeu v1.2.1 release on CRAN

We are very pleased to announce Cluster.OBeu v1.2.1 on CRAN!

Cluster.OBeu is used on OpenBudgets.eu data mininig tool platform with OpenCPU integration of R and JavaScript to estimate and return the necessary parameters for cluster…

Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data “mining” refers to the extraction of new data, but this isn’t the case; instead, data mining is about extrapolating patterns and new knowledge from the data you’ve already collected.

Relying on techniques and technologies from the intersection of database management, statistics, and machine learning, specialists in data mining have dedicated their…

It is not only about understanding about statistics, it is also about implementing the correct statistical approach or method. In this brief article I will showcase some common statistical blunders that we generally make and how to avoid them.

To make this information simple and consumable I have divided these errors into two parts:

1. Data Visualization Errors (Erroneous Graphs)
2. Statistical Blunders Galore (pun intended)

Data…

In the wake of ZMOT (Zero Moment of Truth) it becomes pivotal for any product company to choose the most appropriate advertisement channel for the promotion of their products. This not only helps the organizations to maximize their chances of creating the best first impression but will also help them to be discovered by today’s tech savvy consumers.

Today we will talk about a very…

At Grakn Labs we love technology. Here is our December 15th edition by Filipe Pinto Teixeira, where he looked back at Predictive Analytics.

[Introduction of Association Rules]



Sometimes, the anecdotal story helps you understand the new concept. But, this story is real. About 15 years ago, in Walmart, a sales guy made efforts to boost sales in his store. His idea was simple. He bundled the products together and applied some discounts to the bundled products. (Now, it became common practices in marketing) For example, this guy bundled bread with jam, so that customers easily found them together. Moreover,…

The Data Mining Group (DMG), a vendor-led consortium of companies and organizations developing standards for statistical and data mining models, announced the general availability of version 4.3 of the Predictive Model Markup Language (PMML).

Chicago, IL 8/2/2016 – The Data Mining Group is proud to announce the release of PMML v4.3. PMML is an application and system independent XML interchange format for statistical and data mining models. The goal of the PMML standard is to…

Machine Learning? Data Mining?



Well, there is a little bit difference between machine learning and data mining although I don't see any difference between them.

See the Stackexchange debate on the difference between machine learning and data mining.



At the end, it is about training the machine to…

Complex stuff! However, we can start from the basics , gain confidence in understanding our dataset and utilise whatever has already been done by other stalwarts! 

We have a 11 years monthly data on milk procurement by more than 100 milk co-ops spread across India. This contains their geographic location, quality of milk procured,  price received , products made etc etc. This is a seasonal industry  in India affected by weather, prices and in general crafty management acumen…

Data Science is the system used to extract insights from data that’s mined from various sources. Using various techniques including predictive modeling, Data Science helps to analyze and interpret vast amounts of data. The people who apply Data Science to manage large amounts of data are called Data Scientists. Let’s see how Data Science correlates with the…

If you’re relatively new to Machine Learning and it’s applications, you’ll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around.

Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine…

This blog post was originally published as part of an ongoing series, "Popular Algorithms Explained in Simple English" on the AYLIEN Text Analysis Blog.

Picture added by the…

From episode 10 of my Naked Analyst Channel on YouTube.

I think I do - and it is the ‘appification’ of analytics. What I mean by this is the reduction of a complex analytic activity such as market segmentation, down to a single button on your computer interface. Very much like the…

The Telecom market is saturating and to increase their turnovers, operators are very creative and launch many innovative offers combining devices (mobile phone, MP3 players...), software (music downloads,...) and network (IP calls, ...). On another level, operators would first like to set trustworthy relationships with their good clients and therefore identify them and propose them quality services. To do so, the advanced knowledge of the customers’ past experience is fundamental. In fact,…