Data science approach to organizing my playlist

A couple of years ago I created a Spotify’s playlist where I add all tracks I liked, just as the main repository of things I’d like to listen to, no matter the mood I was when I added that song. As time goes, this playlist became less enjoyable to listen due to the change in rhythm - From listen to a Metal song it jumps to Bossa Nova, which is very annoying. This post contains a few data science approaches I applied to organize this playlist and what worked and what didn’t.

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Where to Find Datasets to Learn Big Data & Data Science

Sometimes you just need data to learn how a algorithm works, to run a stress test or just to have a excuse to spin up several machines in a cluster and see how it crush the data. More often than not, it is incredibly hard to obtain data, and a few colleagues I’ve talked about had similar problem, so this post is a collection of links and references for datasets I know have been open source. Please contribute =)

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Vagrant + Spark + Zeppelin a toolbox to the Data Analyst (or Data Scientist)

Recently I built an environment to help me to teach Apache Spark, my initial thoughts were to use Docker but I found some issues specially when using older machines, so to avoid more blockers I decided to build a Vagrant image and also complement the package with Apache Zeppelin as UI. This Vagrant will build on Debian Jessie, with Oracle Java, Apache Spark 1.4.1 and Zeppelin (from the master branch).

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Create your own dataset consuming Twitter API

Several tutorials have an assumption you own a data set. Often that is not the case and you just can’t take advantage of the tutorial because you don’t have data to play along. To comply with social networks Terms and Conditions you can’t publish your data sets, but you can create your own! Follow through these few commands.

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How to create interactive tweets heatmaps

This posts shows how to create heatmaps of conversations taking place on Twitter, this is a proof of concept technic to learn more about our current datasets, this knowledge would be latter applied to the product development cycle. My objective here is to share a simple way to create a quick visualization and be able to make an internal demo.

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