EC9C3: Topics in Industrial Organisation and Data Science
Principal Aims
This module presents an overview of selected topics in industrial organisation and data science methods. It aims to teach students to understand, appreciate, and, ultimately, contribute to frontier research.
Principal Learning Outcomes
Have a strategic overview and a detailed understanding of developments in industrial organisation and data science methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and background reading. The summative assessment methods that measure the achievement of this learning outcome are: 2 x Submitted assignments.
Develop a critical knowledge of recent research in some key developments in industrial organisation and data science methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and background reading. The summative assessment methods that measure the achievement of this learning outcome are: 2 x Submitted assignments.
Autonomously pursue their own original research agenda in the forefront of the field of industrial organisation and data science methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures and background reading. The summative assessment methods that measure the achievement of this learning outcome are: 2 x Submitted assignments.
Syllabus
Illustrative topics might include:
• Industrial Organisation: Introduction to Structural Models, Models of Demand, Supply and Firms’ Conduct, Market Structure.
• Data Science: Model Selection, Supervised Learning, Unsupervised Learning, Case studies of papers in economics, statistics and natural language research that use data science methods.
Context
- Optional Module
- L1PL - Year 2
- Pre or Co-requisites
- Satisfactory completion of MRes year 1
Assessment
- Assessment Method
- Coursework (100%)
- Coursework Details
- Assessment 1 (50%) , Assessment 2 (50%)
- Exam Timing
- N/A