About Favio Vazquez

Favio Vazquez is a physicist and computer engineer working on Data Science and Computational Cosmology. He has a passion for science, philosophy, programming, and music. He is the creator of Ciencia y Datos, a Data Science publication in Spanish. He loves new challenges, working with a good team and having interesting problems to solve. He is part of Apache Spark collaboration, helping in MLlib, Core and the Documentation. He loves applying his knowledge and expertise in science, data analysis, visualization, and automatic learning to help the world become a better place.

Favio Vazquez Posts (25)

• The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph - 25 Jun 2019
  
  Before being able to develop a Data Fabric we need to build a Knowledge-Graph. In this article I’ll set up the basis on how to create it, in the next article we’ll go to the practice on how to do this.
• The Data Fabric for Machine Learning Part 1-b – Deep Learning on Graphs - 11 Jun 2019
  
  Deep learning on graphs is taking more importance by the day. Here I’ll show the basics of thinking about machine learning and deep learning on graphs with the library Spektral and the platform MatrixDS.
• The Whole Data Science World in Your Hands - 05 Jun 2019
  
  Testing MatrixDS capabilities on different languages and tools: Python, R and Julia. If you work with data you have to check this out.
• Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint - 24 May 2019
  
  Social media has been gold for studying the way people communicate and behave, in this article I’ll show you the easiest way of analyzing tweets without the Twitter API and scalable for Big Data.
• The Data Fabric for Machine Learning – Part 1 - 21 May 2019
  
  How the new advances in semantics and the data fabric can help us be better at Machine Learning
• What’s Going to Happen this Year in the Data World - 14 May 2019
  
  "If we wish to foresee the future of mathematics, our proper course is to study the history and present condition of the science." Henri Poncairé.
• The 3 Biggest Mistakes on Learning Data Science - 06 May 2019
  
  Data science or whatever you want to call it is not just knowing some programming languages, math, statistics and have “domain knowledge” and here I show you why.
• Data Science with Optimus Part 2: Setting your DataOps Environment - 16 Apr 2019
  
  Breaking down data science with Python, Spark and Optimus. Today: Data Operations for Data Science. Here we’ll learn to set-up Git, Travis CI and DVC for our project.
• Data Science with Optimus Part 1: Intro - 15 Apr 2019
  
  With Optimus you can clean your data, prepare it, analyze it, create profilers and plots, and perform machine learning and deep learning, all in a distributed fashion, because on the back-end we have Spark, TensorFlow, Sparkling Water and Keras. It’s super easy to use.
• Learn How to Listen: One of the hardest parts of being a data scientist - 15 Feb 2019
  
  Listen, Be Humble, Be Present and Transform ideas. A Data Scientist will spend a large amount of their time in meetings where you can understand the business, the goals of the area, their KPIs, and their requirements.