Dr Nazanin Khazra\, Toronto
With the growing demand for big data skills in academia and indus try\, there is an increasing need to adapt economics curricula to equip stu dents 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 pose s a significant challenge. This paper addresses this gap by proposing a cur riculum development approach that integrates economic research and data ana lytic skills\, generative AI\, and chat-based models to enhance the teachi ng of big data and ML skills in economics.
The paper outlines the cha llenges in designing courses focusing on economic applications and coding p roficiency. Among these challenges are difficulties in evaluating students’ assignments\, incentivizing original thinking\, and\, most recently\, inte grating generative AI and GPT models as means of learning. By examining ava ilable resources and their utilization in Tech-Econ education\, I identify opportunities to bridge the gap between traditional economics and modern da ta-driven methodologies.
In addition to curriculum adjustments\, the paper explores strategies for fostering teamwork\, addressing diverse stude nt backgrounds\, and training teaching assistants to support the learning p rocess effectively. Using data from the job market postings\, I highlight s tudent motivation\, driven by the high demand for economists with data scie nce skills and the prospect of meaningful research output as a crucial aspe ct of teaching big data and ML courses
LOCATION:IONA 533 GEO:49.260872;-123.113952 URL;VALUE=URI:https://economics.ubc.ca/events/event/nazanin-khazra-toronto- applied-economics-teaching/ END:VEVENT BEGIN:VTIMEZONE TZID:America/Vancouver BEGIN:DAYLIGHT TZOFFSETFROM:-0800 TZOFFSETTO:-0700 DTSTART:20240310T100000 TZNAME:PDT END:DAYLIGHT END:VTIMEZONE END:VCALENDAR