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.
It is a self-paced online course, with automated ticks which you must complete by early in Lent term. There will be no written exam.
Arrangements
- Briefing lecture (available on recordings tab)
- For questions, please use the help forum on Moodle.
- There will also be helpdesk sessions on Wed 17 and 24 Jan, 1–4pm in the Intel Lab. EXTRA: another helpdesk session has been added, Sat 20 Jan, 12–2pm in the Intel Lab.
- There will be an OPTIONAL hints-and-tips lecture on 24 Jan, 1.30–2pm in LT2.
Course contents / interactive tutorials
- 0. Introduction to Python
- 1. Numerical computation with numpy
- 2. Plotting with matplotlib (with plot gallery)
- 3. Handling data with pandas — optional
If you prefer, there are non-interactive versions of these tutorials: Python, numpy, matplotlib, and pandas. If you want to read further, I recommend From Python to Numpy and Scientific Visualization: Python + Matplotlib, both by Nicolas Rougier. There are also some handy recipes for data cleanup (reading SQL, scraping a webpage etc.) if you want to do your own data science investigations.
Assessment / ticks
There are four ticks, each marked pass/fail. Most students are expected to pass all 4 ticks. Each tick contributes 2% to your mark on the maths paper, giving a total of 8%.
- Exercise 0 (getting started) — not assessed
- Tick 1 and Tick 2 — deadline 12nooon on Monday 22 Jan
- Tick 3 (plotting) and Tick 4 (investigation) — deadline 12noon on Monday 29 Jan
Please upload your work to Moodle. Instructions about what to upload are on Moodle. 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 29 Jan.
Running Python and Jupyter
Many 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.