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.
Azure Notebooks
https://notebooks.azure.com/ek358/projects/data-science-pnp-1920For 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.