Favio Vázquez

About Me

Favio Vázquez, physicist and computer engineer, is Data Scientist at BBVA Data & Analytics. He works on Big Data, Data Science, Machine Learning and Computational Cosmology. Since 2015, he's been part of the Apache Spark collaboration, with some minor bug fixes, and improvement of documentation.

The two sides of Getting a Job as a Data Scientist

Are you a Data Scientist looking for a Job? Are you a Recruiter looking for a Data Scientist? If you answered yes or NO to this questions you need to read this. Hope this post will help everyone in the Data Science world. Let’s join together and help each other transform the world into a better place. Remember to have fun and that there’s much more in life than work, I love what I do, but take time for your family and friends, be happy and be kind to one another.

The Data Fabric for Machine Learning – Part 1

Machine learning is not new, but there is a new paradigm to do it, and maybe it’s the future of the field. Inside of the data fabric, we have new concepts like ontology, semantics, layers, knowledge-graph, etc; but all of those can improve the way we think about and do machine learning. In this paradigm, we discover hidden insights in the data fabric by using algorithms that are able to find those insights without being specifically programmed for that.

The Data Fabric for Machine Learning Part 1-b – Deep Learning on Graphs

It’s possible to run deep learning algorithms on the data fabric by deploying graph neural nets models for the graph data we have, if we can connect the knowledge-graph with the Spektral (or other) library. Besides standard graph inference tasks such as node or graph classification, graph-based deep learning methods have also been applied to a wide range of disciplines, such as modeling social influence, recommendation systems, chemistry, physics, disease or drug prediction, natural language processing (NLP), computer vision, traffic forecasting, program induction and solving graph-based NP problems.

The Data Fabric for Machine Learning. Part 2: Building a Knowledge-Graph.

The fabric in the data fabric is built from a knowledge-graph, to create a knowledge-graph you need semantics and ontologies to find a useful way of linking your data that uniquely identifies and connects data with common business terms. The knowledge graph consists in integrated collections of data and information that also contains huge numbers of links between different data. The data here can represent concepts, objects, things, people and actually whatever you have in mind. The graph fills in the relationships, the connections between the concepts.

The Harvard Innovation Lab

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