1. Daniel Nelson
donelson@ncsu.edu
Education
➢ MS Economics, North Carolina State University, December 2014
➢ BS Joint Mathematics and Economics, UC San Diego, 2011
Projects
➢ Predicting Yelp Reviews with KNN. Designed Python program to query and scrape yelp site (html, xml),
designed and implemented KNN algorithm in Python to identify restaurants similar to target restaurant. Used KNN and
sentiment analysis of user reviews to recommend users who are likely to give the target restaurant a high rating, and
predicted what those reviewer ratings would be.
➢ Intergenerational Income Transmission Dashboard. Used Python and SQL to create database of PSID
survey participants income, and fatherson pairs. Used R to generate graphical output, and FFMPEG to display the
graphical output as a movie (“groovy”) to show dynamics. Output is on www.economicsbythenumbers.wordpress.com.
➢ Twitter Sentiment and Hashtag Analysis. Used Python and Twitter API to analyze tweet sentiment and
hashtag usage by state (US) and by time of day published. Created an algorithm to score words not in AFINN.
➢ Mobile App Design. Created and designed a word game, “Hippothetical,” for Android mobile phones. Project
involved creating and maintaining a MySQL database. Used Java and XML.
➢ Modeling Adaptive Learning Behavior with a Bayesian Hierarchical Construction of Expectations. I
propose a method to model learning behavior based on a Bayesian process. I show that doing so influences model
dynamics, but more importantly, it gives the modeler an empirical way to measure past expectations, and their convergence
to the realisation of those expectations. Used R to implement Gibbs sampler from scratch, to generate graphics, and
FFMPEG to create “groovy” Paper available at http://donelson.ddns.net/bk/yourway. Advisor Dr. Alyson Wilson, NCSU,
2013
➢ Regulating Investment Banks Equity Capital: Another Unconventional Monetary Policy Tool. Examines
the effects of regulating Investment Banks equity capital reserves using measures of financial market stress. Explored this
rule in the context of a DSGE, and calibrated using General Method of Moments (GMM) and Federal Reserve data with
Matlab and Dynare. Paper available on request. Masters Thesis, Advisor Nora Traum, NCSU, 2014.
Programming Experience
➢ Experienced (2+years) in R, STATA, Matlab, LaTeX.
➢ Proficient with Python, SQL, Java. Can move seamlessly between Python, R, and SQL.
➢ Familiarity with BI software like Tableau, Excel
➢ Comfortable with a wide range of data types and API’s.
Statistical and Mathematical Programming Experience
➢ Have implemented machine learning algorithms such as Gradient Descent, Random Forests, SVM, and clustering
algorithms like KNN and Kmeans in Python, R and Matlab.
➢ Bayesian modeling, including MCMC sampling techniques and associated diagnostics in R and SAS.
➢ Frequentist modeling, including GMM, OLS, Time Series Analysis with Matlab and STATA.
Conference Participation
➢ NCSU Graduate School Symposium, 2014. Was one of four graduate students in the Economics department
nominated by the Economics Faculty at NCSU to present a paper (Thesis) at the Symposium.
➢ Macroeconomics Seminar, 20122013. Attended weekly seminars at NCSU led by Prof.’s John Seater and Nora
Traum. Guest lecturers included Professors from Duke, UNC, Penn, and various Central Bank economists.
Work/Volunteer Experience
➢ Statistical Consulting, JuneJuly 2014. Analyzed the results of a survey administered to 500 participants by a SJSU
business professor. Was subcontracted by the original consultant to assist with the statistical modeling, feature selection,
and coding. Used STATA and Excel.
➢ Tutor, 2008’13. Tutored Ordinary Differential Equations, Linear Algebra, Physics, Univariate and Multivariable
Calculus, Mathematical Statistics, Programming for R, and SAT prep. Volunteered at an urban San Diego high school’s
weekend “Calc AP Boot Camp”, designed prep materials and metrics to track student performance.
➢ Student Intern, City of San Jose, August 2007’09. Performed cost benefit analysis on work done in public rights of
way. This included compiling data on equipment rental rates, labor rates, and materials costs. Was responsible for
supervising construction crews and inspecting material/work.
