52 Weeks of Data Science - Week 2

To be honest, I had zero motivation to do much data science-related stuff this past week. In part, it was due to travel and seeing family. I almost didn’t want to post something this week, but I feel like I should be consistent with this, and the consistency will hopefully bring more discipline along with it.

Why keep doing this?

This question popped up a bit. If I’m not having any motivation to continue this, why should I? I thought about dropping this whole thing, in week two… But! I’ve been thinking about what motivated me in the first place, and was reminded of a time fairly recently when data exploration helped me solve a bug.

Without going into too much detail, we had a section of code that would either schedule an event or fire it off immediately. One day, the events just stopped occurring seemingly at random. After some exploration of the event data using SQL, we were able to determine it was the scheduled events that were not able to go off. It seems simple in hindsight, but in the moment it was… perplexing. The fix for that is probably a story for another time, but this reminded me that being able to explore and analyze data is an important skill software developers can utilize to spot patterns and trends that lead to more robust and optimized software.

Going forward

I’m going to focus on honing my data analysis skills. I think it’ll be an important tool that I can continue to reach for. I’m going to continue to find datasets and practice finding trends and patterns. I am planning on pivoting the language I use, though. My thought had been to use R, but Julia appeared on my radar again, so I think I’m going to play around with that for a while. In reality, the languages I use don’t really matter. At work, I’m probably just going to use SQL for the most part, but touring other available languages won’t hurt.