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Nathan Byron Colbert 23 West 82nd Street, Apt. B | New York, NY 10024 | 863.327.2634 | nbcolbert@gmail.com TECHNICAL SKILLS • R: Tidyverse, Analysis, Visualization, Data mining, Webscraping, R Shiny • Python: NumPy, SciPy, Pandas, SciKit-Learn, MatplotLib, Seaborn, Jupyter, Tensorflow, Plotly, PySpark • SQL: PostgreSQL, MySQL • AWS EDUCATION Columbia University New York, New York Master of Arts in Quantitative Methods for the Social Sciences: Focus in Data Science; GPA: 3.96 May 2018 Thesis Title: “Neural Style Transfer: The Final Frontier of Electronic Music?” Relevant Coursework: Data Mining | Time Series, Panel Data, and Forecasting | Data Visualization | Modern Data Structures Data Analysis | Bayesian Statistics | Introduction to Venturing (Columbia Business School) Launch Your Startup: Tech (Columbia Business School) The New School for Jazz and Contemporary Music New York, New York Bachelor of Fine Arts in Jazz Performance May 2016 PROJECTS Neural Style Transfer: The Final Frontier of Electronic Music (Master’s Thesis) • Developed experiments to test novel applications of neural style transfer to spectrograms of audio • Utilized Jupyter notebooks to automate the conversion from audio to a spectrogram using short-time Fourier transform to train a convolutional neural network, ultimately, restoring the resulting spectrogram to audio using the Griffin-Lim algorithm The Better Bettor https://nathancolbert.shinyapps.io/BetterBettor/ • A simple R Shiny web app that allows the user to experiment with the Kelly Criterion as a betting strategy through simulation Visualizing Harmonic Variance Among Romantic Era Composers https://nathancolbert.github.io/DSHosting/about.html • Prepared an extensive data set on 9 composers through converting raw midi data into a form useful for harmonic analysis • Created a leaflet map, line graphs and bar charts, and a D3 network graph to explore harmonic variation among composers Visualizing the Winter Olympics https://nathancolbert.github.io/DSHosting/index.html • Visualized various statistics pertaining to the Winter Olympics over time using ggplot2 and plotly Capturing the Confidence in Questions about Confidence • Applied single and multiple linear regression, and ordinal logistic regression to define archetypes among respondents to the General Social Survey Performativity of Machine Learning Algorithms in Predicting Bass Motion in Bach Chorales • Built, tested, and compared the predictive accuracy of random forests, neural networks, and bartmachine WORK EXPERIENCE Columbia University - Arts and Sciences IT New York, New York IT Technician August 2017 - present • Repaired 130+ hardware, software, and network issues • Led a team in refining and restoring hardware for the Columbia Experimental Laboratory in the Social Sciences • Interfaced directly with Columbia executives and professors Columbia University - Institute for the Study of Human Rights New York, New York Research Assistant May 2018 – August 2018 • Developed web scrapers in R to extract valuable funding information on previously unrecorded volunteer efforts for the refugee crisis along the Balkan Route. • Cleaned and analyzed data using dplyr and tidyr • Utilized NLP best practices to study keywords and sentiment of fundraising text to detect relationships between verbiage and campaign success • Visualized data interactively using ggplot2 and plotly PROFESSIONAL DEVELOPMENT Udemy – The Complete SQL Boot Camp, Python for Data Science and Machine Learning Udacity – Introduction to Deep Learning

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