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As an aspiring master of all things data I often think about my favorite aspect of the many things one can do with data: Visualizations (data viz)! If you’ve ever taken time to visit one of the many websites using data visualizations (like https://pudding.cool/ or https://setosa.io/#/) and play with their charts and graphs you can quickly get a sense of the potential and power that data visualizations can unearth. In my personal work with data I’ve used a combination of Matplotlib and Tableau to make vizualizations but I’ve always been intrigued with flexibility and power of the JavaScript library D3.js…


Welcome to part 2 and entry number 5 in my Tableau tutorials series. In my two part series we are looking at how to group your data. There are countless scenarios where one would want to use groupings of some sort, but you can think of groupings as a way to get a more detailed view of some subset of your data. For example, if we are using a COVID-19 dataset we may want to see only cases that include people from the ages of 50 to 60. There are a few ways we could go about viewing that particular…


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Welcome to the fourth entry in my ongoing Tableau tutorials series. This blog will be a two part journey with this entry covering groups and hierarchies and next week’s entry covering sets and dynamic groupings using calculations and parameters. In the past blogs I’ve covered basic data-connection-to-dashboard, calculations, and filtering while touching super briefly on groups and hierarchies. In this post I will be drilling down into a more detailed explanation of groups and hierarchies.

When working with large datasets you may end up with more columns than the Parthenon. For our sanity and ease of analysis we want to…


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This blog marks the third entry in my ongoing “Teaching Tableau” blog. In our previous installments I showed how to create a basic dashboard from start to finish and how to work with filters. This week’s tutorial will go over “Calculations”. What is a calculation? Seems simple but Tableau has three designations for what a calculation is. Calculations are written either as part of the query made on the data source or after the query. This will make more sense in a second. There are Basic Calculations, Level Of Detail Expressions (LOD), and Table Calculations. Basic calculations can be aggregate…


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In my last blog post I touched on a few basics for building simple graphs and dashboards. In this blog post I intend to dive a bit deeper into customizing graphs to make them interactive through the use of filters. For this post I will be using a data set that I created myself from insideschools.org. This data set is a collection of 22,662 comments from all non-charter public schools in New York City. To begin let’s take a look at some ways to filter categorical variables.


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GOAL OF THIS BLOG

Upon completing Flatiron’s data science program I found that I had a solid range of experience with the standard libraries and programs for a data science stack like Pandas, Numpy, Matplotlib, ScikitLearn, etc. Although libraries like Matplotlib and Seaborn can create static and interactive data visualizations it seemed like most businesses were using Tableau in their production line for reporting on insights gained from the data. To make myself industry ready I decided to take some time and learn some of the ins-and-outs of Tableau’s data visualization platform. The following series of blog posts will be a learning journal to…


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Since the time of early civilizations the human impulse to record data has manifested and evolved from tick marks, to hieroglyphs, to cloud-computing databases filled with millions of entries. Our forms of data collection and science have evolved to meet our needs as a society in a way that has benefited the human race in immeasurable ways. …


According to the Oxford Dictionary of Phrase and Fable, the phrase “Garbage in, garbage out.” has been used in the realm of computer science since the 1950s to mean poor programming input will yield poor results because computers can’t think for themselves and self adjust in a relevant way. This adage rings particularly true in the processing of text data. Having gone through the process multiple times now I wanted to write a short guide to serve as a rough checklist on how to pre-process text for input into a machine learning model or for techniques like LDA topic modeling…


Policing issues are and have been a constant dark cloud hanging over the nation since municipal police departments first formed in the late 1800s (and before with more informal forms of “community policing”). The air around policing has become thick with distrust and anger on both sides of the issue. Data science has an interesting role in the debate, as I believe the best we can do to begin to chip away at the problem is to be impartial and speak to the facts. …


As a former NYC public school teacher I can say from personal experience that lack of attendance can be a tricky dilemma to solve. Some students’ guardians work night shifts and get to bed right before a student is about to wake up, or they may leave to work before a student even wakes up to get ready. Some students may even be in a purely neglectful situation, may be living in a shelter, or may live far away and have difficulty finding transportation. With a gamut of challenges to get students in their seats it is troubling to know…

Kevin Macias-Matsuura

Former English teacher turned Data Scientist/Analyst interested in data, design, and storytelling.

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