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…

By Pranay Agarwal and Betsy Romeri.

“They just don't understand me. I provide them with all the necessary details, all the data and analysis possible, but they don't seem to get it.” These were the comments of a data scientist at one of the largest CPG companies in Brazil as he vented to me over coffee and described how frustrating his last year had been. He definitely was feeling the heat from the business stakeholders. None of the major analytics initiatives that his team…

Who is Data Scientist?

“…

Any organisation needs talented, hardworking and skilled employees irrespective of department, business unit or a team. But finding and nurturing such talent can be challenging sometimes. When it comes to data science field, with rapid change and demand in the technology, many organisations have set up the data science teams. A successful data science team has 3 major strengths, A-availability of data, B- infrastructure and most importantly C - the “right” data scientists. 

The…

The analysis and classification of ordinal categorical data are central in most scientific domains and ubiquitous in governments and businesses.

Examples of ordinal data are either found in questionnaires for measuring opinions or self-reported health status. A well-known example of ordinal data is the Likert Scale [1]

(DISLIKE = 1, DISLIKE SOMEWHAT…

Written by Allison Sullivan, PHD - Civis Analytics

Product management resources often focus on collaborating with engineering and design. However, products are increasingly powered by data science, and getting data science into production means teams need…

Autonomous driving is the way to go forward.

Here is my article based on some of the work we have done in this field.

- Python-tensorflow based deep learning model for object classification trained on a novel data-set

- Trained and deployed on an embedded computing platform for real-time object detection…

An emerging trend in the private equity space is an enhanced focus on data science.

This focus has historically been more on the operations side (post-acquisition) where data scientists have been leveraged to help companies improve performance in many key areas including marketing, business intelligence, financial analysis, and human resources. The advantage of focusing data scientists on the operations side is rather obvious: after an acquisition has occurred, the data challenges and…

Before implementing a program, I like to first develop a logic model.  

This can be a fast exercise that can lead to insights into the strengths and weaknesses of the program design.  There are many different models used and I prefer a simple one with 4 basic parts:

1. Inputs—resources that go into a program including staff, materials, money, equipment, facilities,…

In business, we often implement programs and then try to determine if the program had an impact. This is an example of program evaluation where, more generally, program evaluation is a study conducted with the intent of determining how effective a given intervention (program) is at achieving a specific outcome.

What types of programs? That could be a cross-sell program, customer retention program, new advertising method etc.

Is this just A/ B testing? A/B testing is a great…

Data science had broad applications across many different industries. 

If we focus on industries that are in the business of buying (some or all) of a company, then trying to improve the operations before selling then we can identify at least three critical stages for data science to play a significant role.

1. Early Exploration: Mining databases for trend and customer insights
2. Enhanced Pre-acquisition Analysis: Linking early exploration…

We tried to do XYZ. Did it make a difference?”

Whether you are in the for-profit world or the not-for profit world, this is a very basic question that many people try to answer.  

You could be working at a bank trying to figure out which offer is most appealing to customers, at an online retailer figuring out which ad display gets the most clicks, at the Department of Education trying to test the effect of smaller class sizes, at the city government office trying to see if the…

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…

Understanding the Role of Analytics in Insurance Industry

The insurance industry is all about assessing risk and managing the same successfully. Life insurance industry operates intrinsically by balancing risk assessment and risk management. Compiled with a large volume of data the insurance industry operates with, arriving at meaningful…

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…