skip to primary navigationskip to contentScientific Computing Practical Course
About this 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. There will be no written exam.
- You should have one supervision, early in Lent term. There are no exercises for the supervision
— it's purely to help you along with your work on the ticks.
- For any questions, please use the help forum on Moodle. There will also be a help session on Tuesday 26 January 2--5pm [Zoom link].
Course contents
Where to run your code
- You can do your work on hub.cl.cam.ac.uk.
This has all the relevant Python libraries installed. Make sure when you start a new notebook that
you choose
python39
, the latest version of Python.
- You can also do your work anywhere else, for example your own machine with Jupyter or VSCode, or on Google Colab.
The autograder runs anywhere.
- You don't have to follow any template — you should just create your own notebook from scratch
and start working.
However, there is a simple template available in the notebook library.
Assessment
- 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.
- The deadline for
completing the ticks with the autograder, and
for submitting your notebooks on Moodle, is 23:59 on 2 February 2021.
- You will be assessed on the answers, not on the neatness of your code.
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.