Our 4-month training at the Data School has come to an end. It’s the perfect time to recap what we’ve learned so far and what we’ve accomplished through the training. When I reflected back on my struggles early this year trying to learn Tableau myself, I am really amazed by what this training period has taught me. Most of all, I’ve become a lot more confident in digging through unknown datasets for insights. In this reflective post, I will write about (1) My struggles before joining the Data School (2) Training at the Data School (3) Client Projects, and finally (4) Dashboard Week.
Before Joining the Data School
Before joining the Data School, I had been reverse-engineering Viz-of-the-Day on Tableau Public Gallery to learn the style and the tricks. I was fairly good at imitating and making a ‘cool-looking’ dashboards for Tableau public. However, I was very intimidated by Tableau calculations and only used simple functions like IF and some mathematical formulas. I still remembered watching Youtube videos explaining the calculations and secretly admired the Youtuber for using REGEXP. To amateur eyes, it looks like some super-advanced functions. When I made my final-round application for the Data School, I almost pulled my hair out trying to understand and use Level of Details. Even after handing in my application, I still wasn’t very sure if my calculations were really correct.
Other than Tableau, I also really wanted to learn things like API and Webscraping but they very complicated to self-learn especially without using Alteryx. It also took me a very long time to join 2 datasets together and to cleanse them using Python, partly because I was new to it and couldn’t memorize the syntax. Every time I tried to do something, it was lots of googling for the code. At one point, I had to keep a template Jupyter notebook for copying & pasting Python codes for some of the common cleaning tasks I usually do.
Training at the Data School
When I reflect on how insecure I was trying to learn things myself, I realized how valuable the training at the Data School has been so far. The four-month training includes 2 components, learning in the classroom, and client projects. In this section, I’ll be mainly writing about the learning aspect. Some of my favourite topics include:
(1) Tableau & Alteryx – Data Investigation & Profiling (e.g checking the level of grain etc)
(2) Tableau – Table Calculations & Level of Details Made Easy: while you can learn these through blog posts and courses, our coaches shared tips for making them very simple. My colleague, Leona, and Romy wrote a lot about these 2 topics.
(3) Alteryx – API: pulling data through API using Alteryx is super simple, thanks to the JSON parsing tool. I wrote about this process in How to create a density (heat) map: Tableau vs Alteryx and Behind the scene: “Meet the Southern Resident Killer Whales”
(4) Tableau & Alteryx – Spatial Analysis: I wrote a lot about mapping in my blogs so you can tell I love spatial analysis such as 6 ways to enhance your Tableau maps or Behind the scene: The life of an urban vs rural dwellers
(7) Data Modelling
During the 4 months period, we have had 7 client projects. I especially love the diversity of the project we’ve done, from Survey Data, Spatial Analysis, to Web Scraping, and Predictive Modelling. We also took turns to lead a project each, and gain valuable leadership experience. Although there were lots of late nights and panicking moments before the deadline, being able to work with my teammates and delivering the presentations to the clients has been very rewarding. I only truly appreciated the experiences we gained through client projects in our final project. Without the experiences during previous projects, it would not have been possible to create the dashboard.
I also love how we can still do creative things and experiment new things that we didn’t learn during our training. For example, I learned Webscraping using Python’s Selenium Packages during one of the projects and it has proved very useful in subsequent projects.
Another component of the training is the Dashboard Week. Every day, we had to build one dashboard on a selected topic by the coaches and blog about them. While the idea of one dashboard per day sounds easy, it was one of the toughest weeks of the training. During client projects, we were usually only really busy on Thursday (and sometimes Wednesday), right before the Friday presentation. During dashboard week, every day felt like Thursday. Nevertheless, below are three of the most valuable things I learned during the dashboard week.
(1) Vizing about topics I had no knowledge about in a short time frame: this was my main challenge during the dashboard week. We had to make dashboards about the UFC (where I didn’t even know the rules), or air quality data (where I didn’t really understand some of the pollutants such as its sources or safety level), or whale sightings (where I didn’t understand what’s the significance of it). The dashboard week has trained me in the habit to go and do a little bit of research on the topics before I start my data analysis. This has proved extremely useful during the analysis and finding stories. It also helped me to do a bit of a sanity check if the data is consistent with my findings for the background research. If it isn’t, it prompts me to try to understand why, maybe it’s a problem with the data quality or perhaps it’s a problem with my data prep process.
(2) Timeboxing: since we had a strict deadline of one day, the real challenge was about balancing quality and time. I can’t say I’ve nailed it but it was a good practice.
(3) Experimenting with unfamiliar design and features: I usually just go for a 2×2 charts dashboard for most of my projects. It’d still be my go-to layout when I’m short for time. During the Dashboard Week, we had the opportunity to go and look for other community’s inspirations and experimented with different layouts and chart styles. This has been a lot of fun!
Overall, the training has been a lot tougher than I expected before joining the Data School. However, it has also been an extremely valuable time where I learned tons of new skills, formed meaningful relationships with my colleagues, and gained practical experience.