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WHO Tuberculosis Data & ggplot2
So it has been a while since my previous post on some data taken from the UNHCR database. This post we’ll bring it back to the topic of infectious diseases (check out my other posts on the SIR model and MRSA typing). For this post, as similar to previous ones, I give a guide through... Read more
Farmer’s Markets
A series of data visualizations on Farmers’ Market data from data.gov. Dataset Properties Name Location (lat, long, city, state, address) Hours of operation Different goods categories and a yes/no if they’re available at that market UNDERSTANDING THE DATASET I made the following graphic to understand the distribution of the 29 goods... Read more
Popular Music Lyrics Have Become More Negative Over the Decades
This post is guest-written by Alex Lacey, a student at The Ohio State University. It was inspired by the ideas (and used some of the code) from this previous Big-Ish data post. Popular music is constantly evolving, and the changes it has undergone over the last few decades are quite... Read more
A D3 Image Is Worth A Thousand Words: Interview With Morgane Ciot
Editor’s Note: Morgane, Data Robot and ODSC have been working together for a few years bringing Data Science insights and capability to the masses, so if you missed Morgane’s Talk at ODSC EAST 2017 Check out ODSC West 2017’s Visualization Track. Many things have been said and done in... Read more
Intro to D3
Abstract: Visualizations are the most important way we can intuitively make sense of our data. And the best way to dig deeper into our data is to make those visualizations interactive. Everyone from the New York Times to Google is using interactive visualizations to tell stories and analyze data.... Read more
The inspiration for this post is a joint venture by both me and my husband, and its genesis lies more than 15 years in our past. One of the recurring conversations we have in our relationship (all long-term relationships have these, right?!) is about song lyrics and place names.... Read more
This is the second article in our two-part series on using unsupervised and supervised machine learning techniques to analyze music data from Pandora and Spotify. Introduction As you may recall from the previous post I did, where I applied dimensionality reduction and clustering techniques to a set of songs I liked on... Read more
Last Saturday, in the UEFA Champions League final (think of it as Europe’s Super Bowl), Spanish giants Real Madrid defeated their Italian counterparts Juventus FC 4-1. It was a thrilling match, that saw both sides staking an equal claim to winning the match in the first half, with Madrid eventually prevailing... Read more
Pandas & Seaborn – A guide to handle & visualize data elegantly
Here at Tryolabs we love Python almost as much as we love machine learning problems. These kind of problems always involve working with large amounts of data which is key to understand before applying any machine learning technique. To understand the data, we need to manipulate it, clean it,... Read more
Regular Expression & Treemaps to Visualize Emergency Department Visits
It’s been a while since my last post on some TB WHO data. A lot has happened since then, including the opportunity to attend the Open Data Science Conference (ODSC) East held in Boston, MA. Over a two day period I had the opportunity to listen to a number of leaders... Read more