skip to primary navigationskip to content

Course pages 2020–21

Data Science: principles and practice


There will be six short practicals contributing 20% to the final module mark. These will be `ticked' rather than graded: i.e., for each assignment, 100% of the mark is awarded for satisfactory completion and 0% for inadequate work or failure to submit. This does NOT mean that the answers have to be completely `correct', (there is anyway often genuine disagreement on some aspects of e.g. dataset preprocessing), just that a reasonable and informed attempt has been made.

Students are free to work on these practicals after they have been uploaded to this page after the preceeding lecture but should attend the practical session (on Zoom) in order to complete the work and obtain their 'tick'. As a rough guide students should spend no more than 4 hours per practical.

  • Practical 1: Linear Regression (Scikit-Learn) Deadline 4pm, 10th November.
  • Practical 2: Classification I (Scikit-Learn) Deadline 4pm, 12th November.
  • Practical 3: Cassification II (Scikit-Learn) Deadline 4pm, 17th November.
  • Practical 4: Deep Learning I (TensorFlow) Deadline 4pm, 24th November.
  • Practical 5: Deep Learning II (TensorFlow) Deadline 4pm, 26th November.
  • Practical 6: Visualization Deadline 4pm, 1st December.

    Further information about the practicals can be found here (when ready):


    Take-home Assessment

    There will be an assessed and graded final practical based on the material covered in the lectures and previous practicals. Students will write a practical report that will consist of a description and evaluation of the work done of not more than 5000 words excluding tables, graphs and images. It will contribute 80% of the final mark. The deadline for handing in completed reports to student administration (uploaded to Moodle) is Tuesday, 4pm, 19th January, 2021.