Completed an immersive 12-week data science bootcamp with a strong emphasis on project-oriented problem-solving, data wrangling, statistical modeling, machine learning, and communication of deliverables.Project highlights:1) Generating Ambient Music From Raw Audio Using Neural Networks - Trained a recurrent neural network called SampleRNN to generate novel ambient music - Utilized an open source PyTorch implementation of SampleRNN and trained on AWS instances2) The Varieties of Drug Experience According to NLP - Performed topic modeling on subjective recreational drug experience reports - Examined distribution of topics over 15 different recreational drugs - Scraped text data from the website erowid.org using BeautifulSoup3) Predicting the Outcome of Traffic Stops - Applied both gradient boosted decision trees and logistic regression to data curated by the Stanford Open Policing Project to investigate how demographic factors influence the risk of arrests as outcomes of traffic stops in the state of Florida - Utilized AWS to manage the size of the data (~5m observations) - Visualized results using Tableau4) Predicting Beer Ratings on BeerAdvocate - Built a regression model to predict consumer ratings of beers on BeerAdvocate.com - Scraped data from BeerAdvocate.com using BeautifulSoup