Love Data Week Day Four: Explore Data
One of many Dear Data postcards exchanged by Giorgia Lupi and Stefanie Posavec
Today, we're looking at ways to explore your data (this is one of the most exciting parts of working with data, in our completely unbiased opinion)! There are lots of different ways to get started, for people of all different backgrounds and skill levels. Here are some of our favorites across the board.
- New to data exploration? Not totally confident to dive into writing code? Not sure what type of data exploration you want to do? Try Tableau! It’s a no-code required, free to use tool that will allow you to try out a wide range of types of visualization in one place. Tableau is great for beginners, but also great for seasoned data devotees who want to test out different kinds of visualization on a new dataset very rapidly. Check out our past resources on Tableau for more.
- Tip: you can also check out Rawgraphs (https://rawgraphs.io/) for an option that is open source and usable from your browser - no download necessary!
- Looking for a spatial way to explore your data? Try out QGIS (the free and open source GIS desktop application), ArcGIS pro (a commercial GIS application, 1 year free academic license for members of UPenn through the organizational subscription), or ArcGIS online (for access, ask for Penn organizational account setup, email: firstname.lastname@example.org). We have resources for you to learn about these tools here, and past workshop recordings and materials here.
- Interested in a more interpretive, artistic approach to interacting with your data? Try out some of the methods that Giorgia Lupi and Stefanie Posavec explore in their project Dear Data. You can draw, journal, send postcards, and more! We even have a workshop recording and resources to get you started here.
Whatever tool and method you choose to explore data today, you can use guiding questions to keep your exploration thoughtful and meaningful. Consider:
- What does this exploration show me about my data that I didn’t know before?
- Does this exploration support theories that I previously held about my data, or contradict them?
- What are three main takeaways that I can see about my dataset from this visualization? How would I communicate them to someone else?
Enjoy your journey, fellow adventurers! We can't wait to see what you learn and make.