Wow, another year has passed and so much has happened in the meantime!
During my job at the Institute for Transport Research at the German Aerospace Center (DLR) in Berlin I not only worked on the theoretical underpinnings and actual development and implementation of micro-scale traffic models but was obviously also involved in publicizing the results of said models and also other research work. I did this mostly with R, Shiny, PostgreSQL/PostGIS, QGIS and the occasional line of Python code sprinkled in-between. They’re all great. I love them with all my heart and enjoy every second I’m working with one of them. But I found it increasingly hard to visualize data easily and quickly while still being pretty. Sure R and
ggplot allow for camera-ready plots, Shiny and Leaflet make it increasingly easy to put together interactive plots and maps. But sometimes fiddling with their settings and writing the necessary code is just not practical to get to the point quickly. Also, during the fascinating stage of exploratory data analysis (kind of the first date with your new data in the data analysis process…) I felt focusing too much on the code and other technical aspects which distracted me from what I was originally doing: exploring my data to get a better understanding. Going back to the dating analogy it’s like over-thinking what to order and what small-talk topic to bring up next and thereby losing the interest of your possible future partner instead of being focused exclusively on him/her. Not a recipe for success…
It occurred to me that there is a number of tools out there that deal with exactly this topic: visualizing data for analysis. Some of them are similarly hard to use as the aforementioned programming/scripting languages, but there was this one company I had had on my radar (and list of blogs to follow) that promised to do things differently: Tableau. To make a long story short, after I found out that Tableau does have an office in Germany I ended up applying there in late 2015 and am now working as a Sales Consultant out of our office in Frankfurt. (Actually, it’s been six months to the day today!)
Making this change came with lots of thoughts on my end and questions by my friends, family, colleagues and peers. They followed two basic themes: a) do you really want to end your academic career now that you invested so much (time and money) into getting your PhD and working for a renowned research facility?; and b) haven’t you always been a great proponent of open source software, so how do you justify working for a commercial software company? Both are absolutely valid and have been internal struggles for myself as well. The great thing about Tableau is, though, that it is a company founded (and still largely run) by three academics, selling a product that was originally based on scientific research by them and that still has a number of top-notch research folks among them to continue having the products based on the latest scientific findings. The number of highly-trained academics and PhDs among the technical staff is staggering – so I actually feel very comfy and among my kind. As for the second point: well, yeah, that’s a valid point. Tableau costs money. That is, there’s even products that can be used for free, as long as you share the output with the public. On top Tableau is very active in the area of promoting students and researchers with the Academic Programs. And even for the commercial products, Tableau is proud to spend almost one-third of it’s revenue to fund research & development – a ratio unheard of in the commercial software space.
Now there’s a few things this blog post is supposed to be and a few it’s not supposed to be. I will not publicly account for the decision I made (even though the last paragraph might sound like it), nor will I use the post or the overall blog as a marketing platform for Tableau. (I’m not being paid for writing this personal blog, by the way.) What I do want to do instead is to write about my findings, cool stuff I encounter while working with or around data. Working for Tableau luckily doesn’t mean to be entirely disconnected from the data science, visualization and analysis world. Quite the opposite, rather: Tableau fits greatly into any existing data analysis workflows – it’s not an one-ring-to-rule-them-all tool to fit all your needs, instead it integrates nicely with other tools and platforms and plays well together with them. For my part I was ultimately sold when I learned about the mapping features and the R integration in Tableau. And so even though I’m working for Tableau now I still open R Studio, QGIS, pgAdmin and all my other favorite data science tools on a regular basis. So expect a lot (well, I’ve written that before, haven’t I…) of content about data analysis and data visualization in general!
In order to kind of visually represent this reboot of the blog I also gave it a new, cleaner, more responsive and faster-to-load-on-mobile-devices look. Also, since I’m living and working in Germany now I decided to start blogging both in English and German from now on. I most likely won’t publish every single post in both languages, and I most definitely will not go back to translate any of the old posts, but expect the occasional German post to crop up.
That said, it’s great to be back – here’s to me finding enough time to write about all the things I’d like to share.