Introductory description

This introductory data science module will introduce core economics students to a wide array of data sources and types and how to work with them. It is intended to provide students with foundation data science skills, working in R.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Topics typically could include, but are not limited to:

1. Introduction: Defining data science, what data scientists do, the data they use, and the limitations of data science.
2. The data science methodology (E.g., CRISM-DM, TDSP, Domino, etc.)
3. Data sources and types – rectangular vs non-rectangular data (e.g., Textual data, multimedia data, spatial-temporal data, click stream data, etc.).
4. Working with data in R
5. Data extraction and acquisition
6. Getting data into shape (mining, wrangling and manipulation)
7. Statistical methods with big data
8. Data visualisation and analysis
9. AI Applications in Data Science (E.g., Supervised Machine learning, Unsupervised Machine Learning, Deep learning, etc.).
10. Data science tools: (E.g., Working with Git, RStudio, Tidyverse, etc.)
11. Data science application in economic analysis – Literature evidence.

Learning outcomes

By the end of the module, students should be able to:

• .....understand and apply a range of supervised machine learning techniques—such as LASSO, random forests, boosting, and feedforward neural networks—and use causal inference methods to analyse economic questions. Students will also be able to implement these methods in practice by preparing data, building models, and conducting causal analysis using statistical software such as R or Python.

Indicative reading list

Reading lists can be found in Talis

Research element

Apply programming and data analysis skills to define and analyse economic and policy problems, formulate concepts and hypotheses, and show how they are tested in the policy context.

Interdisciplinary

The specific data science skills developed can also be extended beyond the economics discipline. Particularly, this module draws heavily from the field of programming and information technology, and interacts with the socioeconomic and political aspects of evaluating policy and practice.

Subject specific skills

Applied Economics
Economic information
Research and debate
Analytical reasoning
Analytical thinking and communication
Creative thinking
Critical thinking
Policy evaluation
Strategic thinking

Transferable skills

Data-based skills
IT skills
Numeracy and quantitative skills
Research skills
Information technology
Math, Statistical, data-based research skills
Written communication
Coding Skills (Python)