Applying to the Data School has been one of the most unique application processes I've ever gone through. While I'm proud of my resume, I found the merit based review process to be authentic, real and clear; I didn't even share my resume in my application. I also loved how much I learned in the process—not just about Tableau and datasets, but also about how I approach ambiguity, learning, and decision-making.
If you’re just starting your application and feeling overwhelmed, here are five things to consider:
#1: Think About What Motivates You
Ask yourself: What would make this process worthwhile even if I don’t get in?
For me, the answer was helping someone solve a problem. That gave me a sense of purpose beyond the application outcome and helped me stay motivated.
I also really wanted to learn a new school. The first time I touched Tableau was for the application. So I knew at the very least I would leave this process with at least one dashboard on my Tableau Public that I was proud of. I would also learn if this line of work wasn't for me. Thankfully, that wasn't the case!
#2: Don’t Be Afraid to Iterate and Ask Different Questions
Since I knew that the job description for a consultant at the Information Lab is to 'help people make sense of data' I wondered, 'what kind of data do people need to make sense of? So I asked friends what kind of data might be helpful to them in their lines of work. Often, they responded with a wish list of data that doesn’t actually exist. Not so helpful! My mission was to find existing data and make sense of it - not create new data!
I learned the hard way: when you ask someone what data they wish they had, it might not help you find something usable. Be ready to reframe the question and adapt.
#3: Practice Getting to Know Data Quickly
I didn’t do this at first, but I highly recommend it: when looking at a dataset, ask yourself “What does one row represent in this data set?” or "What represents a unique value in this data set?"
In the NYC dog license dataset, for example, each row represents a single dog license. You’ll see details like the dog’s name, breed, gender, and birthday—plus the license’s issue date, expiration, and zip code.
This simple question helps you understand the structure of the dataset—and whether it might be fun to explore.
#4: Don’t Be Afraid to Pivot
My original project idea didn’t work out. After some trial and error, I happened upon a data set about dog licenses in NYC. As someone who frequents a dog park multiple times a day, I knew this was the kind of data set I could have fun with.
Don't be afraid to download a data set, play with it for 30 minutes in Tableau and decide to go in a different direction. Sometimes, the most useful dataset isn’t the most impressive—it’s the one you’ll actually enjoy working with.
#5: Set a Deadline—Then Just Pick One
You can only spin your wheels for so long. At some point, you have to make a choice. I had to let go of the idea that my dashboard had to be useful to someone else. Instead, I reframed it as a learning tool for me.
And it worked—I connected with my neighbors, explored new chart types, and started using Tableau just two months before the application deadline. Even a dataset with only 20 rows and four fields (like some Makeover Monday examples) can help you learn.
This application is not actually about the data set, it's about your process learning more about it.
Final Thought:
At The Data School—and in data work in general—you won’t always have access to clean, exciting, or intuitive datasets. Your job is to make sense of them anyway. So don’t stress too much about picking the “right” one. Just start. Let the process teach you what you need to know.