maestrobrandt,Zachary Brandt,github

My name is Zachary Brandt. I am a data scientist with a background in music and neuroscience. I am interested in conducting academic research in the domains of music, psychology, and data science. I am a creator who loves the entire project pipeline from asking questions, collecting data, analyzing and modeling the data, to creating web apps and slide decks. My goal in life is to create and be a resource to people who, like me, have a passion for music and science.

👋 Hi, I’m @maestrobrandt
👀 I’m interested in music and data science, and finding a company that shares those passions
⭐ I am skilled at Python, SQL, Tableau, and Machine Learning
📫 You can reach me at [email protected] or through my LinkedIn

Zachary Brandt's Projects

dsp

Metis Data Science Bootcamp - Official Prework Repository

gender_differences_in_songs

This project aimed to investigate the topic differences between song lyrics written by male and female artists. The project involved using natural language processing to topic model on the song lyrics, and also included a web application that recommends songs based on lyrics written by an artist of the opposite gender

investigation

Investigation in how to use numpy and scipy to make music

maestrobrandt

Config files for my GitHub profile.

music_and_sensorimotor_coupling

This project aimed to classify music as groovy or not groovy based on conclusions reached by a study at UC Davis on how certain music engages the brain's sensorimotor mechanisms better than others

streamlit-example

Example Streamlit app that you can fork to test out share.streamlit.io

thinkstats2

Text and supporting code for Think Stats, 2nd Edition

topic_modeling_classical_music

This project used NLP to topic model classical music based on musical chords. Classical composers were then clustered using the topics from the NLP, and then a neural network was used to generate original music based on trained music of the different classical composers. All of these metrics were used as a model for complexity of music.

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