Postgraduate Course: Machine Learning, Big Data and Text Analysis for Economists (ECNM11094)
Course Outline
| School | School of Economics |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course provides an introduction to Machine Learning, Big Data and Text Analysis tools, with an emphasis on their applications in Economics. In the first part of the course, students will learn about the most popular methods for classification and prediction, as well as tools for working with large datasets. The second part of the course focuses on practical tools for working with text and string variables, including statistical text analysis methods. The course is delivered through a series of lectures that include in-class Python examples. |
| Course description |
This course provides an introduction to the main methods in Machine Learning, Big Data and Text Analysis and it discusses some empirical applications in the Economic literature. This course combines lectures, in-class examples, and exercises to teach students how to implement machine learning methods with actual data. Examples and studies from the economics literature are used to illustrate some of the topics. While no previous knowledge of machine learning or experience with Python is required, experience with data and programming in statistical packages like Stata is highly recommended. The course aims to provide essential elements required to implement theoretical algorithms using real data but does not provide a deep training in Python.
The first part of the course gives an overview of the most popular methods for classification and prediction and discusses some tools for working with large datasets. Prediction and classification are the two most important tasks that ML can perform. The empirical literature in Economics is increasingly relying on these tasks with two purposes. First, to understand features in the data. Second, to analyse and organize large datasets in order to generate new information in a semi-automated way, a process also known as Data Mining. This new and user-ready information extracted from large and/or unstructured datasets (such as images or text corpuses) is used later to answering economic research questions.
The second part of the course covers a number of practical tools and methods for working with text data and string variables. Text has rapidly become a major source of information for Economic studies, but traditional statistical methods are unfeasible to analyse this type of data. The typical size of these databases and the complex dependencies across the elements in the text (e.g. across words) require that analysis are performed using Machine Learning methods that are discussed in this part of the course.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students should be registered for MSc Mathematical Economics and Econometrics. All other students must email sgpe@ed.ac.uk in advance to request permission. |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2023/24, Available to all students (SV1)
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Quota: None |
| Course Start |
Block 4 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
80 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Exam 80%«br /»
Coursework (computer exercise) 20%«br /»
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Implement the main algorithms for classification and prediction tasks
- Use text data and perform statistical analysis on it
- Organise, process and create large datasets based on semi-structured information
- Understand how Machine Learning methods have been applied into the Economic literature
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Diego Battiston
Tel:
Email: Diego.Battiston@ed.ac.uk |
Course secretary | Ms Grace Oliver
Tel:
Email: grace.oliver@ed.ac.uk |
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