Background

I have worked with R on regressions, complicated machine learning, web scraping / API and text/ sentiment analysis.

I included R to demonstrate my capacity to learn a fairly complicated syntax and to show some familiarity with machine learning.

Azure Machine Learning

I’ve also worked with machine learning on the Azure platform, and I have found it was affordable in smaller less complicated datasets, but the costs could add up at a larger scale.

Applied Work Samples

MLB Initial Statistical Analysis

Looked at the pitch types of ~420 pitchers from 2015-2021 and the effect on era.

Specifically, the pitches were grouped by 3 main types: fastball, offspeed and breaking pitches.

Will need to go to subpage below for the examples (still early on)

MLB Analytics - R

Spotify API Data Pull

I recently used R to connect to Spotify's open API in an effort to retrieve data about my music.

It was seamless and I was able to gather the audio descriptive stats from my playlists. There were many more options but that was all that I needed for the website examples.

Options besides R

For data cleaning, validation and some visualizations – I would prefer Tableau, Power BI and to a lesser extent Excel. As machine learning and data center processing speeds become more affordable I would like to eventually migrate to that space.

All that being said, R still has an incredible appeal in terms of cost management and overall versality when customizing and conducting high level analysis.

I personally love it because it is so flexible.