Department of Computer Science and Technology

Course pages 2019–20

Data Science: principles and practice

Practicals

There will be six short practicals together contributing 20% to the final module mark. These will be pass/fail rather than graded exercises: i.e., for each practical, 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 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, 12th November.
  • Practical 2: Classification I (Scikit-Learn). Deadline 4pm, 14th November.
  • Practical 3: Classification II (Scikit-Learn). Deadline 4pm, 19th November.
  • Practical 4: Visualization. Deadline 4pm, 26th November.

    Azure Notebooks

    https://notebooks.azure.com/ek358/projects/data-science-pnp-1920
    For practical 4: https://notebooks.azure.com/djw1005/projects/dspp

    Github

    https://github.com/ekochmar/cl-datasci-pnp

    Take-home assignments

    Final assignment A is an assessed and graded final practical based on the material covered in the November lectures and previous practicals. Available on Moodle after 25 November. Deadline 5pm, 6 December. Students will write a practical report that will consist of a description and evaluation of the work done of not more than 2500 words excluding tables, graphs and images. It will contribute 50% of the final mark. Please submit your work on Moodle.

    Final assignment B is an assessed and graded final practical based on the material covered in the January lectures. Available on Moodle after 23 January. Deadline 5pm, 7 February. Students will write a practical report that will consist of a description and evaluation of the work done of not more than 1500 words excluding tables, graphs and images. It will contribute 30% of the final mark. Please submit your work on Moodle.