Introduction to Scientific Computing

University of Bristol (2022/2023)

This is a new MSc unit for the new Environmental Modelling and Data Analysis and Geographic Data Science and Spatial Analytics MSc programmes at the School of Geographical Sciences. The unit enables students to effectively utilise widely used tools in Data Science by introducing them to the fundamentals of scientific computing. Such tools include, but are not limited to, Linux shell and command line usage, GitHub and version control, SciPy, basic programming with Python for spatial applications, use and creation of metadata, parallelising code. This is a technical unit and the emphasis is on how to use these tools using a variety of applications both from physical and human domains.

part 1: Python for Geographic Data Science (Seb Steinig)

• week 1: Introduction and unit overview
• week 2: Introduction to Python
• week 3: Code structuring
• week 4 Numerical Python
• week 5: Analysing geospatial datasets
• week 6: Review of part 1 and introduction to assessment #1

part 2: High-performance computing (Tony Payne & Steph Cornford)

• week 7: Profiling, debugging and IDEs (Steph)
• week 8: Version control with git and GitHub (Steph)
• week 9: Linux introduction (Tony)
• week 10: Using high-performance computing (Tony)
• week 11: Parallelising code (Steph)
• week 12: Review of Part 2 and introduction to assessment #2

All of my material (part 1 of the unit) can be accessed as interactive Jupyter workbooks on GitHub.