Hayley Ford Boyce

Data Scientist and Educator

About Me

Growing up in a French immersion atmosphere, I gravitated towards numbers and mathematics due to their versatile and universal application in all languages. This led me to complete a Bachelor of Science degree at the University of Western Ontario from the Applied Mathematics department and subsequently a Master of Data Science from the University of British Columbia. I like to explore a multitude of methods to enhance the efficiency of businesses be it in production, marketing or the financial sector. I am particularly passionate about visualizing these insights by creating easy to understand graphics and tools.

Interests

Data Visualization
Machine Learning
Statistical Analysis
Teaching

Education

Master of Data Science, 2019
University of British Columbia
Bachelor of Science in Financial Modeling and Financial Economics, 2012
University of Western Ontario

Skills and Interests

R

Python

Git & Version Control

Docker

Data Visualization

Machine Learning

Statistics

Data Management

Project Management

Experience

DIGITAL ANALYST

Riversol Skincare

Nov 2017 – Aug 2018 Vancouver, Bc

Conducted A/B testing on numerous advertisement methods, predicting click through rates and customer lifetime value using MS excel.
Successfully predicted customer survivorship with over 90% accuracy. Used multiple statistical distributions attempted to calculate life time customer values and prediction of sales income.
Contributed to the design and creation of the company database and management system. Optimized data retrieval and data maintenance.

Projects

BAIT 509

BAIT509 - Business Applications of Machine Learning is a course offered by UBC’s Sauder School of Business for their Master of Business Analytics program

Data Visualization

An online programming course that teaches how to understand effective data visualizations and perform exploratory data analysis using Altair in Python.

Introduction to Machine Learning

An online programming course that introduces the topic of supervised machine learning for prediction focusing on regression and classification models. This course teaches how to map data to the correct model type, evaluate and select models, as well as communicate and interpret model results.