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Nathan Lovin

Data Analyst

USAC

Biography

I am a Data Analyst at the Universal Service Administrative Company (USAC). Although I have transitioned from the academy to data science, I am still working on my dissertation on nights and weekends. The project covers the political development of the US South during the 20th Century and the impact of these developments on present day politics. I have extensive training and experience in statistical modeling, spatial analysis, and R programming.

An outdated version of my resume is available here.

Interests

  • American Political Development
  • Political Behavior
  • Statistics/Machine Learning
  • MTG

Education

  • Ph.D. in Political Science (expected), 2021

    University of Maryland

  • MA in Political Science, 2018

    University of Maryland

  • BA in Political Science and History, 2011

    Virginia Tech

Skills

R

90%

Statistics/ML

95%

Wizard

50%

Experience

 
 
 
 
 

Data Analyst

Universal Service Administrative Company

Aug 2019 – Present Washington, D.C.

o Analyze large sets of program data using R, Python, and Tableau to identify opportunities to improve the efficiency, integrity, and stakeholder experience of Universal Service program participants.

o Perform research and analysis using programmatic data to relay trends, patterns, statistics to relative stakeholders

o Recommend program process improvements based on data while maintaining reporting standards by ensuring consistency and quality of deliverables.

 
 
 
 
 

Co-Instructor

Georgetown University

Jan 2019 – Feb 2020 Washington, D.C.

o Co-taught a course on data science tailored to public policy students, using R and Python to explore topics such as data wrangling, version control, reproducible examples, machine learning, and text analysis

o Guided students in collecting real-world datasets, designing models, and conducting analyses to determine and articulate policy proposals

o Oversaw the development of machine learning models to evaluate and propose solutions for topics such as improving the efficiency of the New York City public transit system

 
 
 
 
 

Institutional Researcher/Research Assistant

University of Maryland

Jan 2016 – Jun 2019 Maryland

o Collected and analyzed publication and citation data on major political science departments to evaluate the university’s relative academic performance

o Built a multi-decade dataset and evaluated the university’s Ph.D. retention rate as well as the success rate of individual faculty members

o Collaborated with faculty on the collection and analysis of datasets to support research, data visualization, academic writing, and statistical modeling using R, STATA, and ArcGIS

o Managed the data collection process — including overseeing an undergraduate researcher — for a precinct-level primary election project, gathering data from 14 states and creating data visualizations to articulate findings

 
 
 
 
 

Teaching Assistant

University of Maryland

Aug 2014 – May 2019 Maryland

o Designed course materials, planned lessons, provided constructive feedback to students, and led sections in a range of courses, such as American Government and Politics, Introduction to GIS for Social Science Research, and Advanced Statistical Methods for Social Science

o Developed a remote lab process to increase access and efficiency for students and faculty using ArcGIS

o Created dummy and real datasets to demonstrate complex models and mapping processes in R, STATA, and ArcGIS

Recent Posts

A Breakdown of r/EDH's 7/10 Decks

Introduction A couple of weeks ago, there was a post about 7/10 decks on the r/EDH subreddit. I was curious to see what the general …

r/EDH 7/10 Appendix

Projects

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Census Map Tinkering

Just some map tinkering I did a few months ago in R.

r/EDH 7/10 Deck Data

Repo to hold public facing data for the breakdown of r/EDH’s 7/10 decks.

Recent Publications

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The Urban-Rural Gulf in American Political Behavior

Urban-rural differences in partisan loyalty are as familiar in the United States as they are in other countries. In this paper, we …

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