Scientific Computing Practical Course

This course is an introduction to using Jupyter and Python for scientific computing — that is, for data science and machine learning.

Arrangements

• Briefing lecture: Thursday 25 Nov, 4pm, Lecture Theatre 1
• This is a self-paced online course, with automated ticks which you must complete by early in Lent term. There will be no written exam.
• For any questions, please use the help forum on Moodle. Or join the live help sessions over Zoom [Zoom link]:
  Wed 19 Jan, 1–2.30pm. Fri 21 Jan, 3.30–5pm. Wed 26 Jan, 1–2.30pm. Fri 28 Jan, 3.30–5pm.

Course contents / tutorials

• 0. Introduction to Python
• 1. Numerical computation with numpy
• 2. Handling data with pandas
• 3. Plotting with matplotlib
• Appendix: data cleanup recipes

Assessment / ticks

There are two ticks, each marked {0,1,2}. Most students are expected to get a total of 4. This course is worth 7.7% of your grade on the Maths paper.

• Exercise 0: getting started not assessed [github]
• Tick 1: COVID simulator deadline 25 Jan [github] and model solution
• Tick 2: COVID data analysis deadline 1 Feb [github] and model solution

Most students do their work in Jupyter Notebooks on hub.cl.cam.ac.uk, which has all the relevant Python libraries installed. (Make sure when you start a new notebook that you choose python39, the latest version of Python.) Exercise 0 shows you how to use the automated ticker, and has some tips about how to structure your notebooks.

(But you can also do your work anywhere else, for example your own machine with Jupyter or VSCode, or on Google Colab. The automated ticker runs anywhere.)

You will also need to upload your notebooks to Moodle by 1 Feb. You will be assessed on the answers, not on the neatness of your code, so you do not need to spend any time cleaning up your notebooks. A random subset of you will be asked for live ticking, for auditing purposes, after 2 Feb.