Data Science Lifecycle revolves around using various analytical methods to produce insights and applying Machine Learning techniques to do the predictions from the collected dataset. The main objective is to achieve a business challenge.

The entire process involves…

“Many business folks are still wondering about the role and benefits that DevOps automation can provide to big data. If you are also one of these, then this blog has all your answers. Get all your queries sorted here.”

Big Data is a buzzword in the tech world.…

With the start of the Liga Santander in Spain, I share a very interesting R library that I share with the community in social networks  @Vdot_Spain for all the analysts and data scientist of the football world.

According to @Vdot_Spain: "soccergraphR v0.1 MVP is the first step in helping analysts, journalists, and clubs understand and manage @OptaJose information with #Rstats. You can install it from…

This is not very simple to choose a machine learning method and letting it go wild on the data. Particularly, understanding the core business problem and objective of the outcome and frame accordingly is one of the vital factors in machine learning. A general approach is difficult to recommend without intimate knowledge of the data. However, it sounds like we need to formalize the aspects of your model. Following questions may help to decide on machine learning problem or…

One of our business units wants to target the competitors’ customers with personalized product/offer. To do that, Business needs to understand who are the prepaid/postpaid customer of the competitor to push the relevant and personalized product/offer and they don’t have this data. Now, this is a binary classification problem and we want to apply machine learning machine method to predict the likeliness of competitor customer to be prepaid or postpaid.

Steps in Data Science…

Unlike supervised learning, unsupervised learning not working with labeled data, it is not showing the machine the correct answer. Instead, it is using different algorithms to let the machine create connections by studying and observing the data. Learn much of this through study and observation. Learning and improving by trial and error is the key to unsupervised learning.

However, the Knowledge Discovery process is the field of data mining is concerned with the development…

One of the best ways to learn about any topic is start with very fundamental questions like What, Why etc? Good old Socratic method. In this series of articles on data mining, I plan to approach this topic in a similar fashion. 

What is Data Mining?

Simply put, Data mining is the process of sifting through large data sets to identify…

The health area is characterized by the management of huge data volumes. What if those data are processed and provided to the health professionals and their patients or, even to the health system at large? Not only one, two, three patients are healed but also it enables to enhance the population’s…

The proliferation of data and the huge potentialities for companies to turn data into valuable insights are increasing more and more the demand of Data Scientists.

But what skills and educational background must a Data Scientist have? What is its role within the organization? What tools and programming languages does he/she mostly use? These are some of the questions that the…

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…

(A Wake-Up Call to Data Geeks Who Doubt)

by Anne Russell

Anne is the Managing Partner and Founder of World Data Insights, a data consulting company helping customers transform data into information that matters. Anne has spent the last decade immersed in multiple aspects of…

Well, when it comes about Big Data processing, there are a few platforms like Hadoop, mongoDB and Oracle in-memory DB etc. However, I wonder why the platforms must be complicated ? 

Someone think in a same way, please look at following. 

http://en.usp-lab.com

Cheers,

Several members of the Talent Analytics team had the opportunity to attend Teradata's Big Analytics Conference in Boston on May 22. With a great speaker lineup, the educational and networking event offered both a technical and digital marketing track that covered the latest advancements across the big analytics ecosystem and highlighted how data-driven products and…