| Unit name | Data science for economics |
|---|---|
| Unit code | EFIMM0095 |
| Credit points | 15 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Chris Muris |
| Open unit status | Not open |
| Pre-requisites |
ECONM1022 Econometrics; ECONM1008 Applied Economics |
| Co-requisites |
Nil |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
The aim of this unit is to equip students with tools that allow them to practice data science in all aspects of economic practice. This unit emphasizes applications in economics and uses the statistical programming language `R`. This unit will introduce students to the use of data science and computer algorithms to learn from economic data. The topics include the use of `R’ for importing various kinds of data, data management, data visualization, and data-based prediction. This unit will also equip students with the tools needed for the programming of new functionalities to perform complex tasks for which no software packages are available. This unit aims to give the computational tools needed by students to support their use of econometric and economic modelling in the analysis of economic data.
At the end of this course, students will be able to perform the following advanced computing task independently:
1. Use `R` to analyse data for economic applications in line with theoretical economic and statistical models.
2. Load, manage, and visualize complex datasets in `R`.
3. Use existing software packages for advanced data visualization and prediction.
4. Write advanced new functionalities for complex computational tasks, including numerical optimization, and critically assess existing implementations.
Summative assessment: 2 hour written exam (50%). Bi-weekly group assignments (50%)
The first two group assignments are introductions to data analysis in R and focus on learning outcome 2. Assignments 3 and 4 are more advanced, and address learning outcomes 3 and 4. A capstone fifth project will address learning outcome 1. Each week, students are randomly assigned to groups of four (4). All students in a group receive the same grade. Random assignment of students to groups helps to manage problems of free-riding (a group member not contributing but benefiting from the work of the other members in the group) and group conflict.
Formative assessment: Small group computer exercises, class participation and discussion in computer lab tutorials.
The main textbook is:
"R for Data Science"(most recent edition), by Gareth Grolemund and Hadley Wickham.
A physical copy is available as
Grolemund and Wickham (2017), "R for Data Science: Import, Tidy, Transform, Visualize, and Model Data", 1st Edition, O'Reilly.
