This month, I originally set out to write a data journalism article and pitch it to publications. I plan to follow through on this. I plan to use Bayesian point analysis, which I learned about from G. Elliott Morris newsletter about the Democratic debates. This method allows us to see when the rate of events… Read More
Month 2 Master
M2M – July Recap
For July, I set out to build a poster presentation for the Rocky Mountain Symposium on Analytics in Sports. And guess what, mission accomplished! Here’s the paper that I started with: Divya Parmar – NFL Draft Pick Efficiency Here’s the poster output: RMSAS Poster Presentation Printed Version Overall, I enjoyed attending the conference and meeting… Read More
M2M – July Introduction
For this month, I originally set out to do a build a neural network in Python. However, I got serendipitous news that my senior thesis from UC Irvine, which discusses value and efficiency in the NFL Draft, was accepted to the Rocky Mountain Symposium on Analytics in Sports. Instead of doing M2M, I will be working… Read More
M2M – June Recap
For June, I set out to re-introduce myself to Python via a DataCamp course. The DataCamp course I selected, Data Scientist With Python, had multiple modules. I decided to attempt the “Introduction to Python” module, but if it was too easy, then the backup was “Intermediate Python for Data Science.” As often happens in life, curiosity took me in directions… Read More
M2M – June – Update #3
This month I am aiming to re-introduce myself to Python coding. This week, I decided to follow along to Jay Alammar’s post that I found on Hacker News. His write-up focuses on NumPy, a foundational library that unlocks data analysis and machine learning in Python. Let’s import it and get started. A fundamental building block… Read More
M2M – June – Update #2
This month, I am re-introducing myself to Python coding. To make this week interesting, I went off the beaten path and completed a DataCamp’s A New Era of Data Analysis in Baseball course, which uses on Major League Baseball’s Statcast data in a Jupyter notebook. Statcast uses radar technology to track every baseball in every ballpark 20,000 times… Read More
M2M – June – Update #1
This month, I am re-introducing myself to Python via a DataCamp course. I am taking the Data Scientist With Python track, starting with the “Introduction to Python” module. Here’s what I have covered so far: The basic data types are integer, float, string, and Boolean. Lists in Python can contain a mix of data types, including lists. Python indexes start… Read More
M2M – June Introduction
This month, I originally set out to re-introduce myself to Python via a DataCamp course. I have identified the track I will be taking, Data Scientist With Python. I will attempt the “Introduction to Python” module, but if it is too easy, I will do “Intermediate Python for Data Science.” Happy June.
M2M – May Recap
For May, I set out to do a machine learning course in R, and I identified a DataCamp course to take. Originally, I set out to complete one module per week. However, in the end, I completed one module total – one chapter per week. Each chapter of the module is a different supervised machine learning algorithm,… Read More
M2M – May – Update #3
For this week, I continued with my machine learning in R coursework, specifically the Supervised Learning module. The topics were decision trees and random forests. Decision trees function like “if-else” conditions used to make classification decisions. They are good when the creator wants transparency about what went into the decision, such as why a loan… Read More