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The Data Fabric for Machine Learning – Part 2: Building a Knowledge-Graph
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.
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The Whole Data Science World in Your Hands
Testing MatrixDS capabilities on different languages and tools: Python, R and Julia. If you work with data you have to check this out.
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Analyzing Tweets with NLP in Minutes with Spark, Optimus and Twint
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.
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 The Data Fabric for Machine Learning – Part 1
How the new advances in semantics and the data fabric can help us be better at Machine Learning
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What’s Going to Happen this Year in the Data World
"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.
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The 3 Biggest Mistakes on Learning Data Science
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.
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Data Science with Optimus Part 2: Setting your DataOps Environment
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.
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Data Science with Optimus Part 1: Intro
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.
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2018’s Top 7 R Packages for Data Science and AI
This is a list of the best packages that changed our lives this year, compiled from my weekly digests.
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Ontology and Data Science
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.
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The Data Fabric for Machine Learning – Part 1
Data Science with Optimus Part 2: Setting your DataOps Environment
Ontology and Data Science