Graphs, Gradients, and Groupby

Graphs, Gradients, and Groupby

Image by author. Graphs, Gradients, and Groupby. What my first major Data Science project has taught me.

As I wrap up my first project for Phase 1 in Flatiron School’s Data Science Program, I am overcome with the number of things I now know how to do, that I did not even know existed just two short months ago. The pace of this program is intense, to say the least. I find myself constantly juggling the many responsibilities of mom life while trying to fit in as much time as I can to devote to my learning. Essentially, what this means is that my two-year-old is frequently mad at me… and I haven’t been sleeping. (So if anyone wants to buy me an October gift, I’m a size XXXL coffee, thank you.)

However, this project has taught me many things. Before I get into the nitty-gritty codestuff, I need to touch on the fact that working on this project has taught me how to do better in the future. The issue I struggled with, and continue to struggle with, is that I’m a perfectionist by nature when it comes to academics. People who know me are sometimes surprised to hear this, because the standard mess-level in my house is not one that you’d associate with perfection. I write this staring at a room littered with toys and a table decorated with yesterday’s craft supplies. But when it comes to anything I’m learning or being evaluated on, I hold myself to a very high standard. And that has gotten me into trouble here.

The problem? We had two weeks to complete a major project as a group. I did it on my own, started it early, and gave myself about three weeks for completion. But I did three different versions of the project because I kept trying to switch directions and make it better. I kept telling myself I still had time, so I’d use that time to explore other paths. Instead of staying on track and focusing on getting through the necessary tasks, I kept abandoning my data in favor of something new or something that seemed like it would be better than what I had. I imported, I web-scraped, I made API calls, I cleaned and prepared the data — and then I started all over again with something different. In the end, despite the fact that I started early, I ended up rushing to complete the project because I switched directions so many times. This has taught me, or reminded me, a very important lesson, which is to stay on track and not be so hard on myself to make things perfect in the beginning stages. I’ve learned that I am far too old to be pulling all-nighters and eating cereal at midnight while I count how many hours I have until the kids wake up!

projects data-science pandas-dataframe seaborn flatiron-school

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