Teaching

Pfeiffer Beach, CA October 31st, 2015

Pfeiffer Beach, CA October 31st, 2015

As our means of collecting data have improved in the past decade, the amount, size, and complexity of available data has grown exponentially. The availability of this new data significantly affects the way that we analyze and interpret trends that occur in fields ranging from biology - genetics, neuroscience, and personalized medicine - to the social sciences - psychology, managerial science, and finance. As a consequence, the skills needed for practicing statisticians have evolved.

As a statistics instructor, I aim to demonstrate to students the widespread use of statistics and its evolving interplay with our every day lives. At the University of San Francisco, I have the great opportunity to teach in the Master and Bachelor programs of Data Science as well as in the Department of Mathematics and Statistics. My hope is to inspire undergraduates to pursue statistics in their academic and professional careers, as well as help students utilize cutting-edge statistical and computational tools to analyze modern complex data sets.  I have served as Director of the Data Science program since 2017 and have mentored over 20 students in 9-month internships with companies in the Bay Area.

Courses:                                                                             

MSDS 601: Regression Analysis (Website)

MSDS 620: Introduction to Machine Learning      

MSDS 623: Multivariate Statistical Analysis

MSDS 629: Computational Statistics**

MSDS 630: Experimental Design for Data Scientists

MSDS 628: Case Studies in Data Science**

MSDS 800: Statistical Network Analysis** (Website)

MATH 106: Business Statistics  

MATH 370: Probability and Applications  

MATH 372: Linear Regression Analysis

BSDS 100: Introduction to Data Science with R** (Website

**I proposed, developed, and taught this course      

Corporate Training Sessions:

San Francisco 49ers - Classification: An Overview of Methods and Coding in R

Summer Courses:

Network Analysis I: A 4-week summer course at the ICPSR "Network Analysis I" for the ICPSR Summer Program in Quantitative Methods of Social Research in Ann Arbor, Michigan from June 26 - July 21, 2017. See this website for more information. <Online Course><Teaching Evaluations>

Workshops:

Generalized Exponential Random Graph Models: Inference for Weighted Networks: A 3-hour workshop on the foundations, interpretation, and implementation of the generalized exponential random graph models (GERGMs) for weighted networks. R software is used for all applications. Political Networks (PolNet) Conference, 2015 and 2017. <Presentation> <R Vignette>

Intro to Network Analysis: A 2-hour workshop on where networks arise, different types of networks, and how to explore networks to identify communities and structurally important vertices. Data Institute Conference, 2017. <Outline><Brief History><Exploratory Analysis><R Vignette>

Practicum Mentorships:

AT&T Big Data and Center of Excellence (2015 / 2016); Airbnb Data Science (2015 / 2016); San Francisco 49ers (2016 / 2017); Houston Astros (2016 / 2017 and 2017/2018); Eventbrite (2016 / 2017); Silicon Valley Bank (2017); Shippo (2017); Zipcar (2017/2018); Xoom (2017/2018); Bracket Voodoo (2017/2018); Reddit (2018/2019); UCSF Neuroscape (2018/2019)