Pre-course self-assessment questionnaire: Transport Data Science

TRAN5340M

Welcome

This self-assessment questionnaire is designed to help you evaluate your readiness for the Transport Data Science (TRAN5340M) module at the University of Leeds. The module focuses on applying data science techniques to solve real-world transport problems using open source software, with a strong emphasis on reproducible workflows.

Please check one box for each question unless the question explicitly states to “Tick all that apply”.

1 Skills assessment

1.1 How comfortable are you with managing files and folders on your computer?

• Very comfortable (I use file managers like Explorer or Finder to organize deep folder structures and search for files)
• Comfortable (I can perform operations like creating, moving, and renaming folders/files)
• Somewhat comfortable (I can do simple tasks but sometimes struggle to find where I saved things)
• Not very comfortable (I often need help finding files or organizing folders)

1.2 How confident are you with installing software on your computer?

• Very confident (I can install and configure most software)
• Confident (I can install standard software)
• Somewhat confident (I can install basic software but need guidance for complex tools)
• Not confident (I need help with software installation)

1.3 Have you used a command line interface (e.g., PowerShell, Command Prompt in Windows, Mac Terminal, Linux shell) before?

• Yes, regularly (I use it for multiple tasks including file management and installing software)
• Yes, occasionally (I’ve used it for simple commands, such as simple one-off calculations, navigating directories or listing files)
• No, but keen to learn

2 Data science skills

2.1 Data science experience and outputs

By data science tools, we mean editing code using programming languages such as R or Python to work with and modify datasets and to generate results.

I have used data science to produce:

Tick all that apply

[Skip the next few questions if you have only followed tutorials]

• Professional or academic-grade outputs
• Data visualization (e.g., map or chart)
• Clean, processed dataset from raw data
• Statistical analysis or model (e.g., regression)
• Interactive dashboard or web application
• Automated/reproducible report or technical document
• Only followed step-by-step tutorials so far

2.2 Which of the following problems have you solved independently (with or without AI help)?

Tick all that apply

• Working Directories: Fixing “File not found” errors by managing relative/absolute paths
• Data Types: Troubleshooting why a plot fails because a column is a ‘string’ vs ‘numeric’
• Package Management: Resolving failed library installations or version conflicts
• Documentation: Successfully using StackOverflow, reading GitHub issues, implementing instructions from AI tools such as ChatGPT or documentation to fix a specific error message

2.3 Do you have experience with R or Python for data science?

• Yes, extensive (I use them regularly for data analysis)
• Yes, intermediate (I can write scripts and use common packages)
• Yes, basic (I’ve completed an introduction)
• No, but I’m keen to learn

2.4 Which of the following packages (libraries) have you used?

Tick all that apply

• tidyverse or pandas (data manipulation)
• sf or geopandas (spatial data handling)
• Other (please specify): ___________________________

2.5 Have you worked with Quarto, R Markdown or Jupyter notebooks?

• Yes, extensively (I use them for reports and reproducible work)
• Yes, occasionally (I’ve created simple documents)
• No, but I’m familiar with the concept

3 Data science and transport

3.1 How would you rate your ability to load and clean datasets, perform basic data transformations, and calculate summary statistics?

• Advanced (I can handle complex datasets and analysis)
• Intermediate (I can perform most common data analysis tasks)
• Basic (I understand the concepts but need practice)
• Beginner (I’m just starting to learn these skills)

3.2 Have you worked with spatial/geographic data before?

• Yes, extensively (I work with spatial data regularly)
• Yes, occasionally (I’ve used GIS software or spatial packages)
• No, but I’m interested in learning

3.3 Are you familiar with any of these transport data sources?

Tick all that apply

• OpenStreetMap (OSM)
• UK road safety data (Stats19)
• Census travel to work data
• Origin-destination (OD) matrices

3.4 Experience with version control and code sharing?

• Yes, I have used Git for version control
• Yes, I use cloud storage (e.g. Dropbox/OneDrive) to share code
• No, but I’m keen to learn

4 Next steps

After completing the questionnaire, use the following guide to assess your readiness and prepare for the module.

4.1 Key resources

Topic	Resource
R	RStudio Primers / R for Data Science
Python	Python for Data Science / Pandas Guide
Spatial	Geocomputation with R / Geocomputation with Python
Transport	Transport chapter (R) / Geocomputation with Python
Git	GitHub’s Git Guide
Terminal	The Linux Command Line
Advanced	Reproducible Road Safety / Advanced R

4.2 Final preparation

3. Install required software (R/RStudio, Python, Git) before the first session.
4. Check you have access to the GitHub repository at https://github.com/itsleeds/tds and try downloading materials and/or asking a question in the discussions section.
5. Prepare any questions you may have for the first session.

4.3 Contact information

If you have questions about your readiness or need guidance on preparing for the module, please contact:

• Module leader: Robin Lovelace (R.Lovelace@leeds.ac.uk)
• Teaching team: See the module website for full details.

Best of luck with your preparation! We look forward to seeing you in the module.