Applied Economics and Big Data (P.Grad.Dip)

Overview

Course Overview:

The Postgraduate Diploma course in Applied Economics and Big Data is a one-year full-time programme commencing in September 2024.
Its aim is to introduce students to the application of state-of-the-art quantitative and statistical skills required to use Big Data in applied economics. Graduates from the Postgraduate Diploma in Applied Economics and Big Data at Trinity will acquire valuable skills which are sought after in the high productivity sectors, including information technology, consulting, financial services, risk assessment, and logistics. Students will become fluent with popular (and demanded) programming languages, such as Python, R and STATA and will be able to handle and analyse the large amount of data which is being created every day. Further, the students will have gained working knowledge and application experience in basic/advanced regression and statistical analysis, machine learning and important applications such as economic forecasting and text analysis. This course aims to train students who have limited quantitative research backgrounds but strong interests in enriching their methodological toolkit to contribute substantively to evidence-based, big data research activities in the modern economy.

Is this course for me?

This program is designed for students passionate about applied economics and big data who aim to learn advanced skills in analytics and quantitative methods for future careers as economists, data analysts, or financial analysts.

Career Opportunities

The Postgraduate Diploma in Applied Economics and Big Data offered by the Department of Economics at Trinity College Dublin opens a plethora of career opportunities in a data-driven world. Graduates can embark on careers as economists, data analysts, or financial analysts in various sectors including finance, consulting, technology, and government. They may work in roles that require economic forecasting, market research, policy development, or strategic planning. With a focus on Big Data, graduates are uniquely positioned to leverage large datasets for robust economic analysis, setting them apart in sectors where data-driven decision-making is paramount. Furthermore, the skills gained can be a stepping stone to advanced research or academic careers in economics

Course Structure

This course comprises eight (13) taught modules for a total of 60 ECTS.

1. Introduction to Statistics and Regression Analysis (5 ECTS) - Core
2. Introduction to Big Data for Economics (5 ECTS) - Core
3. Microeconometrics (10 ECTS) - Core
4. Macroeconometrics (10 ECTS) - Core
5. Spatial Economics and Big Data (5 ECTS) - Optional
6. Machine learning for economists (5 ECTS) - Optional
7. Text analysis for central banking (5 ECTS) - Optional
8. Impact Evaluation and Big Data (5 ECTS) - Optional
9. Labour markets and Big Data (5 ECTS) - Optional
10. Development Economics and Big Data (5 ECTS) - Optional
11. Environmental Economics and Big Data (5 ECTS) - Optional
12. Financial Markets and Big Data (5 ECTS) - Optional
13. Quantitative Text Analysis for Social Scientists (5 ECTS) - Optional

Course Content

The one-year program covers a wide range of core modules aimed at equipping students with state-of-the-art applied economics methods.
 In their first semester, students will take modules in microeconomics, macroeconomics and econometrics. In their second semester, students will have the chance to choose various elective modules, drawn from different fields of theoretical and applied economics as well as modules in machine learning and quantitative text analysis.
 All modules are taught around weekly lectures and tutorial sessions and assessed with applied continuous assessments (no exams or final dissertations are required). Students are expected to bring their own laptops (Mac/Windows/Linux) for use in lectures and tutorials throughout the course. Tablets are not suitable for this course.
 
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