Diego Arenas, ODSC

Diego Arenas, ODSC

Data Technologist

Bio: Desarrollo soluciones de negocio con plataformas de BI para agregar y generar valor al negocio con tecnologías y herramientas de BI. Soporte a la toma de decisiones. Conocimientos en herramientas como Business Objects, Pentaho, Informatica Power Center, SSAS, SSIS, SSRS, MS SQL Server 2000, MS SQL Server 2005, y otros motores de bases de datos. Mis áreas de interés son los Sistemas de Información, en particular el modelamiento de datos, análisis de información y visualización de la información. Miembro Profesional de ACM desde noviembre de 2007. Specialties: Data modeling, data warehousing, data mining, performance management, business intelligence.

Sentiment Analysis on Lyrics

Sentiment Analysis on Lyrics

 This blog post is on song lyric sentiment. Feel free to fork this code from GitHub. Sentiment Analysis is one of the techniques of NLP (Natural Language Processing). It is part of NLU (Natural Language Understanding). It allows us to classify the sentiment of a text, positive or negative, according to the words it contains. […]

ODSC State of Data Science 2017 report: The Data Wave

ODSC State of Data Science 2017 report: The Data Wave

When co-authors D.J. Patil and ODSC East 2017 Keynote Presenter Thomas H. Davenport wrote that sexy article in 2012 they claimed that “Data scientists’ most basic, universal skill… the ability to write code… may be less true in five years’ time.” Now 5 years later, in this post, we will see that this statement is not […]

Introduction to Evaluating Classification Models

Introduction to Evaluating Classification Models

In this post we will describe how to evaluate a predictive model. Why bother creating complex predictive models if 5% of the customers will churn anyway? Because a predictive model will rank our clients based on the probability that they  will abandon the company. It helps answer these two questions: 1. How should we optimise our resources? 2.  What […]

Wading into Deep Learning, a 30 minute query

Wading into Deep Learning, a 30 minute query

In this interview, Jonathan Schwarz of Google DeepMind shares insight on Deep Learning projects. He offers tips and advice for the those interested in DL, and explains whether DL projects relate to other data driven projects? He comments on effective team size, software, frameworks, common mistakes, resources for learning, and more all under 30 minutes. Have a good lunch! Jonathan […]

United Airlines and Data Technologies to avoid “Turbulence”

United Airlines and Data Technologies to avoid “Turbulence”

It wasn’t an overbooking problem. United Airlines was trying to move four flight crew members to the next airport. They forced passengers to get off the plane with the consequences we saw on the video from last Sunday, but don’t take our word for it. Let’s talk data. An elaborate AI, like this, is not necessarily […]

Containers for Data Science

Containers for Data Science

Containers represent a simple way of creating pipelines for data analysis or even data science architectures. In this post, I will explain some of the container features and suggest a microservices architecture for data science professionals. People often describe containers as “lightweight virtual machines”, but it’s a fallacy. Virtual Machines emulate the hardware and OS […]

Scala, the Language for Data Science

Scala, the Language for Data Science

Let’s be honest, there are two reasons why it’s worth learning a new programming language. The first reason is because you will need it for your daily job and the second reason is because it’s fun. The programming language Scala is something you would like to learn by the end of this post if you […]

GO Data Science!

GO Data Science!

Editors Note: In the spirit of accessibility, we will begin offering Spanish translations of select articles, this will be the first. Function to come.  Whether you want to become the best Pokémon master or the best data scientist alive, the path to get there will be quite similar: lot of training, focussed dedication, learning by experience […]