A 3 day data-visualization bootcamp that I designed for professors in my *alma mater*, Tecnológico de Monterrey.

# Introduction

I was hired to teach a data visualization bootcamp on my former university using the programming languages: Mathematica, R, and Python. This was a bit challenging because, even though I am reasonably proficient with the three languages, I tend to prefer Mathematica and Python over R; and jumping from one language to the other on the fly was a bit confusing sometimes.

## Objective

The main objective of the course was to provide datasets and exercises on a wide array of data visualization techniques for course-takers to develop some of the basic skills required to transform and visualize data into meaningful plots. This was not an in-depth course, as it was aimed at participants with different backgrounds and skillsets (from humanities to engineering and computer science); and as such, it was geared towards having the audience take code snippets, put them together and customize them so that they could start developing a sense on which kinds of datasets tend to be better represented by a given plot type.

## Content

In addition to over 20 plotting coding exercises, the course covered these subjects: image formats, data formats and sources, python and anaconda, color palettes, dataviz good practices, github, markdown and HTML, github-pages, ffmpeg. In which the participants alternated between doing exercises and listening to explanations with tips and tricks to convey information more efficiently. This helped keep the bootcamp dynamic, as sessions were 8 hours long, which made it challenging to keep engagement and energies high.

# Select Exercises

## NFL Players Size

I like NFL, so when I got a hold of a CSV file with player’s stats, I decided to use it for one of the exercises. The idea in these panels is to compare the player’s weight and height across the different groups of player positions.

## 1984 Wordcloud and Treemap

To exemplify some ways to do frequency-based representations of data, we did an exercise with the text of my favorite book “1984” by George Orwell.

## Cities

We did some spatial data exercises, including an interactive globe with the largest cities in the world, and a map of the largest cities in the US:

# Topics and Code

As I mentioned before, we did a bunch of exercises. The full list with links to the code can be followed here:

- Time Series: Stochastic traces (Mathematica), Stacked area (Python and R), Digraph (R), Stream chart (R)
- Counts: Box-whisker chart (R), Violin plots (R), Wordcloud (R)
- Scatter: Scatter plot (Mathematica), Bubble chart (Python), Scatter plot with histograms (Python)
- Transitions: Heatmap (Mathematica), Chord diagram (R), Random network (Mathematica)
- Factorial: Density plot (Mathematica)
- Geographic: Leaflet (R), Folium (Python), Globe plotting (R), Bubble map (Python), Fancy map (Mathematica)
- Clustered: Tree map (R), Random networks (Mathematica)

With the whole sitemap available in the following link.

# Code repo

**Repository:**Github repo with all the materials and exercises