Vincenzo Coia

he/him/his 🌈 👨

Assistant Professor of Teaching

UBC - Vancouver

Image credit: Frank Wang Photography

Hi!

I’m an Assistant Professor of Teaching at the University of British Columbia, Vancouver. My priority there is to establish the responsible use of statistics for data science.

My hobbies include outdoor recreation 🌲 such as hiking 👞, birding 🐦, skiing 🎿, fishing 🎣, canoeing 🚣, and others.

Some of the things I do:

I solve problems using statistics, machine learning, and data visualizations.
I interact with students to make data science more approachable.
I use R, git, and friends to help me do these things.

I can also identify birds, and other things about nature.

Here are generic slides I use to introduce myself.

Interests

Distributional Forecasting
Repurposing Statistics for Data Science
Software Engineering
Extreme Quantile Regression
Flood Forecasting

Education

PhD in Statistics, 2017

The University of British Columbia
MSc in Mathematical Statistics, 2012

Brock University
BSc in Biology with Earth Science, 2011

Brock University
BSc in Mathematics and Statistics, 2011

Brock University

More About Me

Teaching Philosophy

What does being an effective teacher mean to me?

Projects

Interpreting Regression

An in-progress book on data-driven problem solving from a probabilistic lens. A modern take to statistics. Title may change.

STAT 545 @ UBC

An open-access course on data wrangling, exploration, and analysis using the modern R landscape with git.

YouTube Channel

Short videos promoting a clean and modern data analysis. Pairs with STAT 545.

coperate

Draw powerful multivariate insights using copulas. R package. Coming soon!

distplyr

Draw powerful insights using distributions. R package.

igcop

Tools for accessing the IG and IGL family of copulas. R package.

rqdist

Build predictive distributions using linear quantile regression. R package.

Tutorials

Feb 18, 2018 3 min read

The missing question in supervised learning

You all know the drill – you’re asked to make predictions of a continuous variable, so you turn to your favourite supervised learning method to do the trick. But have you ever suspected that you could be after the wrong type of output before you even begin? Regression trees, loess, linear regression… you name it, they’re all in pursuit of the mean (well, almost all). But the true outcome is random.

Feb 22, 2017 6 min read

Contour Plots

This tutorial introduces contour plots, and how to plot them in ggplot2. What is a contour plot? Suppose you have a map of a mountainous region. How might you indicate elevation on that map, so that you get to see the shape of the landscape? The idea is to use contour lines, which are curves that indicate a constant height. Imagine cutting the tops of the mountains off by removing all land above, say, 900 meters altitude.

Feb 22, 2017 6 min read

Mixture distributions

This tutorial introduces the concept of a mixture distribution. We’ll look at a basic example first, using intuition, and then describe mixture distributions mathematically. See the very end for a summary of the learning points. Intuition Let’s start by looking at a basic experiment: Flip a coin. If the outcome is heads, generate a N(0,1) random variable. If the outcome is tails, generate a N(4,1) random variable. We’ll let \(X\) denote the final result.

Jan 1, 0001 1 min read

Communicating Data

How to communicate data through visuals. Inspired by Claus Wilke’s “Fundamentals of Data Visualization”.

Slides here

Jan 1, 0001 5 min read

Generalized Additive Models

Generalized Additive Models To fit a GAM in R, we could use: the function gam in the mgcv package, or the function gam in the gam package. Differences between the two functions are discussed in the “Details” section of the gam documentation in the mgcv package. Choose one, but don’t load both! mgcv tends to be updated more frequently, and is generally more flexible (compare the Index pages), so that’s what is used in this tutorial.

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