Teaching Big Data and Machine Learning Skills in Economics
Dr Nazanin Khazra, Toronto
Abstract
With the growing demand for big data skills in academia and industry, there is an increasing need to adapt economics curricula to equip students with essential capabilities such as data generation, web-scraping, visualization, and application of machine learning (ML) methods. However, the lack of established models for teaching such courses in economics poses a significant challenge. This paper addresses this gap by proposing a curriculum development approach that integrates economic research and data analytic skills, generative AI, and chat-based models to enhance the teaching of big data and ML skills in economics.
The paper outlines the challenges in designing courses focusing on economic applications and coding proficiency. Among these challenges are difficulties in evaluating students’ assignments, incentivizing original thinking, and, most recently, integrating generative AI and GPT models as means of learning. By examining available resources and their utilization in Tech-Econ education, I identify opportunities to bridge the gap between traditional economics and modern data-driven methodologies.
In addition to curriculum adjustments, the paper explores strategies for fostering teamwork, addressing diverse student backgrounds, and training teaching assistants to support the learning process effectively. Using data from the job market postings, I highlight student motivation, driven by the high demand for economists with data science skills and the prospect of meaningful research output as a crucial aspect of teaching big data and ML courses