Programming Tools for Urban Analytics URBAN5123
- Academic Session: 2020-21
- School: School of Social and Political Sciences
- Credits: 20
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
This course aims to equip students with the skills to use some important programming tools to deal with some of the challenges of accessing, managing and analysing big data. It also introduces them to best practices in coding, in managing collaborative projects and in working in ways which support transparency and reproducibility.
Classes will run in Semester 2, delivered in three-hour blocks, once per week, over nine consecutive weeks. All teaching will take place in a computer lab.
Students will undertake a data analysis project which requires them to demonstrate the skills which they have acquired during the course. This will be written up and presented along with the scripts they have produced to do the analysis.
The aim of this course is to familiarise students with programming tools which allow them to access, manage and analyse big data efficiently and effectively. It will cover techniques required to set up a database for holding data in a way which enables efficient analysis, as well as tools to extract data from on-line Application Protocol Interfaces (APIs). It will cover best practices in relation to coding, collaborating on coding projects, and reproducibility of analyses. It will also provide an introduction to machine learning tools.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Set up, connect to and query a simple relational database from a statistical package such as R
■ Retrieve and analyse data from an Application Protocol Interface (API)
■ Perform basic machine learning tasks
■ Write code according to best practice and produce tidy data
■ Collaborate effectively with other analysts using appropriate tools
■ Produce documentation for their work which makes the processes behind analyses transparent and reproducible.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.