Projects
CDS SQL App
This application helps users from other institutions create SQL code to query their own electronic health record. This project is apart of a consortium of +10 instituitons around the country. The consortiums interests are looking at the amout of alerts that are firing when opening a patient's record. These alerts happen often and can cause fatigue for the providers. This app will be used by the analytics departments at each institution to dynicmally create standardized SQL code to query their electrontic health record systems and then load the data into a cloud database for further analyses. The user will be able to choose which SQL syntax they requrie and which aspect of the queries they need.
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Basketball Stat Viewer
This project's goal was to pull data from NBA's stat website to then create tables with trends. The trends will be used to make informed decisions when creating betting slips on Fanduel. The data is pulled and wrangled using R, then visulized in a Shiny dashboard for the user to dynamically choose a player of interest.
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NFL Total Opportunities
This project scrapes pro-football reference for the weekly player box scores. Then it aggregates those stats into season datasets. The user can then filter the data by team, and week to see the opportunity break down for individual players. The last tab, will show a visulization of the opportunities on a field. Based on the advanced analytics via NFL.
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Pro Baseball Experience Application
Pro Baseball Experience is an online baseball simulation league with over 100 players. Each user creates a fictional baseball player and grow their stats through tasks in the league forum. I created this app to track the fictional teams and players in the league throughout the seasons. I used R to wrangle, clean and subset the data. I then used Shiny to create a front end solution that the end user can self-serve and look at the league's statistics in a myriad of ways.
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Tweet Interaction Prediction
This was a group project for a graduate level course in software engineering. We were given tweet data from Clinton, Trump and members of congress for a year leading up to the election. We were tasked with creating some sort of data science solution with the data. We decided to try and predict new tweets, using the given tweets as the train dataset. We used linear regression models to find significant words within the tweets, then I created a front end solution for the user to enter in a new tweet and some other variables. The users tweet then goes through the regression models to see what the "favortie" and "retweet" interaction would be if the canadate tweeted the user entered tweet.
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Fantasy Football Analysis
This was an analysis I did for my personal fantasy football league. I then decided to polish and expand the analysis for a final project for a graduate level Python course. The first analysis looks at how changing the scoring a tight end receives could make tight ends just as valuable as the other skill positions. The second analysis attempts to find if there is a correlation to the total points a position scores for a fantasy football team and where that team ends up in the standings.
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New MLB Team Analysis
This analysis was a final project for a GIS course. I took baseball data from Sean Lahman and found hotspots for where to send a theoretical scout to find a hot bed of players. I also used this data paired with census data to find the best place to put a MLB expansion team.
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Predicting MLB Team Wins
Using data curated from Sean Lahman, I attempted to predict how many wins an MLB team could expect to get based on their expected Runs and ERA for the season. This was a final project for a linear regression graduate course. I used linear regression to create the model and also performed analysis to check the validity of my model.
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