Alright, so today I’m gonna walk you through my little adventure with Scott Mitchell’s stats. It all started with a simple question: “How can I get some interesting data out of the good ol’ Scott Mitchell’s work?”
First things first, I dug around for anything related to Scott Mitchell. I mean, I started with a basic web search. Found a bunch of articles, blog posts, you name it. The key was figuring out which resources had the potential for extracting some quantifiable info. This involved reading through summaries, checking dates, and just generally trying to get a feel for the scope of his contributions.
Then, I shifted gears a bit. Instead of just passively consuming the info, I started taking notes. I mean, really detailed notes. I wanted to catalog everything that seemed even remotely “countable”. Things like number of articles published, topics covered, the years when certain projects were active, etc. I dumped all this into a massive spreadsheet.
Next up: Data Cleaning, ugh. This was the most tedious part, no joke. The data was messy, inconsistent, and sometimes just plain wrong. I had to standardize date formats, correct typos, and decide how to handle missing values. This took way longer than I expected, but it’s crucial. Garbage in, garbage out, right?
Now came the fun part! Time to analyze. I used some basic tools to get a sense of the data. Simple stuff, like calculating averages, finding the maximums and minimums, and looking for any obvious trends. I wanted to see if there were any patterns in the topics covered over time or if there were any significant peaks in publishing activity.
I tried a few visualizations. I played around with bar charts, line graphs, and even a pie chart or two. Visualizing the data really helped me spot things I might have missed just staring at the numbers. Plus, it made the data a lot more digestible. I even considered a word cloud to highlight the most frequent keywords, but that ended up being too messy.
Refined the analysis. Once I had some initial findings, I went back to the data and tried to dig a little deeper. I looked for correlations between different variables. For example, was there a relationship between the length of an article and its popularity? Things like that. I tried to see if any of my initial observations held up under closer scrutiny.
Finally, I summarized all my findings. I wrote up a short report outlining the key stats and trends I discovered. I included the visualizations, along with some brief explanations of what they showed. Nothing fancy, just a clear and concise summary of what I learned.
It was a fun little project, even though the data cleaning almost drove me nuts. It’s amazing what you can learn just by digging into the numbers! Hope this helps!
