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:

• Students will have an understanding of the data science methodology, the various data science tools available, and how to answer economic questions using various data types.
• Students will learn to source for non-economic data, clean, manipulate, visualise, and analyse these data using programming techniques in the relevant software package (R) for real-world inspired scenarios.

Indicative reading list

1. Grolemund, G. & Wickham, H. 2016. R for Data Science. Available online: https://r4ds.had.co.nz/index.html
2. Hastie, T.; Tibshirani, R.; Friedman, J. H.; & Friedman, J. H. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). New York: Springer. Ebook and Print Book Available in Warwick Library
3. James, G.; Witten, D.; Hastie, T.; & Tibshirani, R. 2021 (2nd Ed.). An Introduction to Statistical Learning with Applications in R. Springer. Available online: https://www.statlearning.com/
4. Silge, J. & Robinson, D. 2017. Text Mining with R: A Tidy Approach. Available online: https://www.tidytextmining.com/
5. Nosratabadi, S.; Mosavi, A.; Duan, P.; Ghamisi, P.; Filip, F.; Band, S.; Reuter, U.; Gama, J.; & Gandomi, A. 2020. Data science in economics: comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), p.1799.
6. Provost, F. & Fawcett, T. 2013. Data science and its relationship to big data and data-driven decision making. Big data, 1(1), pp.51-59.

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

Students will have the opportunity to develop skills in programming for data analysis; problem solving; computational economics; statistical modelling; analytical thinking, reasoning, and application; critical, creative and strategic thinking; mathematical computing skills; and data presentation and interpretation skills.

Transferable skills

Students will have the opportunity to develop: Data skills; Application of mathematics and statistics in economic analysis; IT skills; Written communication skills; Oral communication skills; Mathematical, statistical and data-based research skills.