Flatiron School: Full-time, 15-Week Data Science Immersive in New York City, 2018-19

Subjects covered:

  • Data cleaning & gathering: Python, Pandas, NumPy, SQL

  • Visualization with Matplotlib & Seaborn

  • Frequentist & Bayesian statistical methods

  • Regression (linear, multi-variate, logistic)

  • Hypothesis & A/B testing

  • Supervised & unsupervised machine learning methods for classification, clustering

  • Time series analysis

  • Regexs & NLP

  • PySpark & big data

  • Deep learning

University of Washington: M.L.I.S., beta phi mu, 2017

Coursework included:

  • Introduction to Data Science (programming in R)

  • Beginner Front-end Web Development (JavaScript, HTML, CSS)

  • Information Retrieval Systems (search algorithms, SEO)

  • Information Structures Using XML (XSLT, XSD, etc.)

  • Independent Study: What Makes 'Quality' Metadata

  • Digital Preservation

  • Qualitative Research Practicum: Disaster Response Information Management

  • Construction of Indexing Languages

  • Research, Assessment, and Design

  • Information Behavior

  • Management of Information Organizations

  • Instructional and Training Strategies for Information Professionals

Reed College: B.A., Classics, 2013


Professional Development Grant (Visual Resources Association Foundation, 2016)

Digital Library Federation Students and New Professionals Forum Fellowship (Digital Library Federation, 2016)

iSchool Jones Information Fellowship (University of Washington, 2015)

Ruby-Lankford Grant for Faculty-Student Collaborative Research in the Humanities (Reed College, 2012)

Commendation for Academic Excellence (Reed College, 2012 & 2013)

Opportunity Grant (Reed College, 2012 & 2013)

Certificates, Extracurricular Courses:

Statistical Foundations for Data Science and Machine Learning (Metis, 2017)

JavaScript Jumpstart (Fullstack Academy, 2016)

Certificate of Achievement: Privacy Literacy (Infopeople, 2016)