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.
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.
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.
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.
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.
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.
In simple words, one can say that ontology is the study of what there is. But there is another part to that definition that will help us in the following sections, and that is ontology is usually also taken to encompass problems about the most general features and relations of the entities which do exist.
The Data Fabric for Machine Learning – Part 1
Data Science with Optimus Part 2: Setting your DataOps Environment
Ontology and Data Science