A few months ago, I was trying to figure out how to create a sunburst chart in Tableau when I came across Ken Flerlage’s blog post on it. Sunburst charts are very pretty but honestly, they are rather hard to create and very fragile if the data isn’t formatted exactly right. But in his post, I came across a link to another blog from Adam McCann where he created an “icicle chart”:
This month, I’m going to try creating a correlation matrix visualization in Tableau. I stumbled upon this correlation matrix by Kelly Tan, a Tableau Public 2020 Featured Author, and really liked how it’s a lower triangular matrix (my old linear algebra days are calling out to me!). I wanted to see if I could figure out how to create lower or upper triangular tables myself.
Now to start out with, I’m going to need some data to be do correlations with. The Bob Ross data in Kelly’s visualization needs some manipulation so I want a more simple dataset so I…
For my very first Data Dissection, I was searching through the various authors featured on the Tableau Public website at this time (December 2020). One visualization I particularly liked was Aparna Shastry’s MM-Wk19-Toughest Sport by Skill from the 2018 Makeover Monday Week 19 challenge. She uses radial bar charts to compare the skill difficulty of different sports as rated by ESPN. Here’s what her visualization looks like:
The use of colors and background was very consistent and appealing to me. The use of the radial version of bar charts made the charts very compact too.
But how would I recreate…
*Note: Although Tableau will be used as the main backdrop for this article, measures and dimensions exist in many other data visualization tools, such as Looker or Google Data Studio.
In Tableau, there exists the concept of measures and dimensions to classify fields you can use. However, it’s easy to get the two confused when working with them. You are able to switch some fields from measure to dimension or vice versa but there is a distinct difference between the two that will affect your visualization. Let’s first start by reviewing the definitions of each term.
*originally published February 18, 2020 on my LinkedIn
I just completed the 6 month part time Data Analysis & Visualization Bootcamp and it was a total adrenaline rush the whole time! I really enjoyed my interactions with the instructor, the TAs, and the material we learned in just 6 months. The curriculum was quite broad, ranging from basic Excel skills to introductory Python to a deeper overview of machine learning than I expected (this is dependent on the instructor and the program as they update their materials over time). I really enjoyed learning all that I did and the homework…
*originally published on my LinkedIn on August 31, 2019
Last weekend, I had the pleasure of participating in the yuuvis hackathon in downtown Austin, TX. I had never participated in a hackathon before but that didn’t matter because guess what, most of the team I was on had never done one either! In fact, many people at the event said that it was their first hackathon. It certainly didn’t stop some of the eventual winners from entering haha.
What was the experience like?
Having never done a hackathon before, I had no idea that to expect really. I did expect…
The pandas library in Python is an essential tool for data analysis, but did you know you can combine it with the Tableau Hyper API to make your pipeline from raw data records to visualizations easier? This is the exact purpose for the pantab library developed by Will Ayd at innobi, which is a Python wrapper around Tableau’s Hyper API. Here, I will go through a guided example of how to use pantab in a data workflow.
pip install pantabin the terminal of your chosen environment
The Data Dissection Doctor