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EC9C3: Topics in Industrial Organisation and Data Science

  • Camilla Roncoroni

    Module Leader
  • Ao Wang

    Module Lecturer
  • Mirko Draca

    Module Lecturer
12 CATS - Department of Economics
Spring Module
Autumn Module

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. 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. 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"

" Enable students to autonomously pursue their own original research agenda in the forefront of the field of industrial organisation. 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
L1PJ - Year 2

Assessment

Assessment Method
Coursework (100%)
Coursework Details
Assessment 1 (50%), Assessment 2 (50%)
Exam Timing
N/A

Reading Lists