Teaching
Since completing my undergraduate degree, I have been fortunate enough to receive opportunities to teach students and leverage my passion for education at the same institution that had cultivated it.
Owing to the COVID-19 pandemic, my time at the University of Sydney shifted quickly from the joys of educating a room full of people, to the challenges of speaking to walls of grey boxes. This did, however, allow for new, fresh approaches, and at every turn I have sought to deliver a learning experience that exceeds expectations, engaging students and supporting them through their highs and lows.
I remain employed as a tutor for both undergraduate and postgraduate units, having taught the following units, in order of first involvement:
“Data Science, Big Data and Data Variety” – a 2nd year undergraduate unit.
This was my introduction to university teaching, focussing largely on learning SQL, with additional exploration of more complex data sources such as web scraping, image analysis, text processing and spatial data.
“Data and Information Management” – another 2nd year undergraduate unit.
Similarly focussed on database management and the basics of SQL, this unit features a more theoretical approach (modelling relationship diagrams, using relational algebra) yet with more technical depth (transactions, security, application development).
“Informatics: Data and Computation” – a 1st year undergraduate unit.
As a first year subject, the basic processes of the data science workflow are foregrounded through Python, with a focus on basics like sourcing data, typical cleaning operations, data ingestion, creating meaningful summaries and investigating trends.
“Computational Statistical Methods” – a postgraduate core unit of the Master of Data Science program.
This subject leverages statistics for modelling and prediction purposes. After teaching the basics of R, a variety of methods are explored, such as clustering, sampling, smoothing, supervised and unsupervised learning, model selection, and more.
