It's end of June 2021, I have been through the Master’s Degree in Visual Tools to Empower Citizens. It has been a wild and chaotic ride, a mix between obstacle racing, code breaking, and meditative retreat. The road has been bumpy but it took me where I wanted: got a crash course -from the comfort of my home desk- into bits and pieces I had been missing and others I did not know I was missing. It was time to get hands dirty again into coding as much as it was time for me to break bubbles.
This is a first tale about the outcome.
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A year ago I was able to sketch, design and produce produce a variety of static visualizations based on concepts and data, as well as editing videos and producing animations; mostly with pen and pencil, Adobe Creative Cloud and Microsoft Office programs . Data wrangling and analysis I did mostly in Excel, and for maps I was using ArcGis. Drafts and final materials I delivered via shared drives. I used a dedicated set of tools to gather, analyse and display networks of scientific activity. RawGraph for down the pipe but not so standard charts and Flourish for anything interactive.
My main tool box would look something like this, here comes the logo soup:
Now I can tackle data wrangling with Phython, R, and relational databases, which makes the process so much more versatile and reproducible than a spread sheet, like when I used to use Wolfram Mathematica years ago. In a big leap, I have reached the place where I can wrangle, prototype and produce visuals in javascript D3 and quite comfortably in Observable notebooks. Cooperative platforms have been a huge addition, from designing in Figma and Observable to build up codes in a team via git repositories. I understand the basic things of web architecture, enough to be able to collaborate to build up a small web page with interactive data-based visualizations in it. I have learnt a bit of html and css on the way. I understand the basics of building data pipelines and the resources needed for processing, storing, and hosting. I think I could manage web scraping as well. Cartography-wise I am now in a different place, being acquainted with a huge amount of open-source resources, which really feels lighter than being tied by the subscription to ESRI products. Also, I finally got a hand on how to obtain and process satellite images. On top of all these, there are countless other online resources and small pieces of software that we have seen, commented, shared or encountered during the master.
This are sure additions to my toolbox:
...tools we have learnt during the master that do not have a logo.
I enjoyed reviewing probability and revisiting data mining methods as statistical models like clustering, principal components, profiling, in addition to the regression process-based models I have been most used to handle. It was a little knowledge refuge for me in the middle of the coding bombarding during the first term. I still think however that there was a lost opportunity to instill the scientific thinking into the workflow, which is one of the most obvious lacks I sense in the field of non-academic data visualization.
Then there was all the discussion about ethics, and it is one of those things you think you know already but then you realize that not really. In the context of empowering citizens this module was perhaps the most relevant!