This was my final project for the “Applied Plotting, Charting and Data Representation” course (part of the “Applied Data Science with Python Specialization” on Coursera). Not the most exciting piece of work, but it was cool I got to use the olympic data I hadn’t really worked with since my SQL specialization. Here is a basic bar graph looking at […]
Pythagorean expectation, attributed to baseball statistician Bill James is a formula originally used to describe a relationship between the number of runs a baseball team scores and allows, and the team’s winning percentage. The basic formula is… Winning Percentage = (Runs Scored)2/((Runs Scored)2 + (Runs Allowed)2) For example1 , the 2002 New York Yankees scored 897 runs and allowed 697. […]
So, when we left off we were looking at population, GDP per capita, and urbanization metrics. Nothing jumped out except the urbanization… (tl;dr the first five posts in this series… I’m looking at connections between population and economic metrics and country success at the olympics. To start at the beginning, check out part 1.) (Standard warning in all my posts. I’m […]
Ok. So I have my main working tables, Athletes and Countries, (finally) done… (tl;dr the first four posts in this series… I’m looking at connections between population and economic metrics and country success at the olympics. To start at the beginning, check out part 1.) (Standard warning in all my posts. I’m a beginner. Nothing below should be taken as the […]
OK. So I just figured out what to do about countries being called different names across datasets are two Congos are now “Congo (Kinshasa)” and “Congo (Brazzaville)”. I worry though, I’ll have to deal with this issue again… (tl;dr the first three posts in this series… I’m looking at connections between population and economic metrics and country success at the […]
And back to work. When we left off I had just built the Countries DATAFRAME… (tl;dr the first two posts in this series… I’m looking at connections between population and economic metrics and country success at the olympics. To start at the beginning, check out part 1.) (Standard warning in all my posts. I’m a beginner. Nothing below should be […]
Ok. Back to work on the capstone for Learn SQL Basics for Data Science (via Coursera). I’m trying to look at the connections between a country’s population and economic metrics and success in the olympic games. I’ve collected all the data, gave it a quick look, and now it’s time to dig in. (If you want to see how I […]
So we’ve come to the last course in the Learn SQL Basics for Data Science specialization from UC-Davis (via Coursera), the capstone project. Here we supposedly take what we learned during the other courses and make something. Really, though, in the early days of this course I’m still learning a lot. So I’m writing this series of posts as a […]
(What is this project I’m writing about? I have a pretty detailed description here.) The first thing I’m noticing is that I’m taking this project very seriously. This is a project that I feel like I could provide a minimally acceptable product and pass and move on. I’m not doing that. I think there are 2 reasons: This is a […]
So, with pandemic kicking in, I decided to do what most people in the world decided to do, study Python and start playing disc golf. My disc golf career is of to a spectacular start, but you’re probably not here to read about that. Many newbies, including myself, who wanted to study Python and were either not afraid of paying […]