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Course pages 2020–21

Scientific 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 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.


  • 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.