Today was one of those days where everything just clicked a little more. I’m still new to the world of AI and data science, but every session brings a new layer of understanding—and today was all about digging into datasets and making sense of them visually.
I started off with something pretty simple but powerful: getting to know the data. I used Pandas to explore the dataset by calling .info() and .describe(). It’s amazing how much you can learn about your data just by running these two lines. I got to see the column names, data types, counts, mean, min, max—you name it. It gave me a quick snapshot of the dataset’s overall structure.
Then I moved on to cleaning the data, which is a pretty important step before doing anything fancy. I checked for missing values and duplicates. It’s surprising how often these issues pop up, and if you don’t deal with them early, they can totally mess up your analysis later. I’m starting to understand why people say that 80% of data science is cleaning and preparing the data.
But the real fun started when I brought in the Seaborn library.
I began experimenting with scatterplot() and pairplot(), and wow—this is where the data really came to life. Seaborn makes it super easy to build beautiful visualizations that help you actually see relationships in your data. At first, I was a bit confused about when to use scatterplot versus pairplot. But after playing around, I finally got it:
scatterplot()is great for focusing on two specific variables and seeing how they relate. It’s clean, direct, and good for simple comparisons.pairplot()takes things up a notch by creating a grid of scatter plots for every pair of variables in the dataset. It’s super helpful when you want a big-picture view of how everything connects.
Overall, today felt like a solid step forward. I didn’t just write code—I actually understood what I was doing and why I was doing it. The more I learn, the more excited I get about this AI journey. Can’t wait to keep going and see what’s next.