Courses / Module

On successful completion of the module, students should be able to:

• Know how to uncover useful information from messy data;
• Be aware of various ways in which data are collected and compiled;
• Be aware of the differences between observational and experimental studies;
• Have a knowledge of the biggest challenges currently facing data security and privacy;
• Be familiar with image and text data;
• Be familiar with common methods used in predictive analytics for making predictions about future unknown events;
• Be informed about model evaluation and validation approaches with the goal of understanding how we can make our models as generalizable as possible;

• Tutorial & Labs/Practical hours are subject to review by the Head of Department.

Delivery methods	Hours
Lectures	36
Labs / Practicals	0
Tutorials	10
Planned learning activities	0
Independent student activities	142
Total	188

• Module Graded (numeric value) or Ungraded (Pass/Not Passed): Graded

Assessment type	Options	Weighting	Duration
Continuous Assessment		35%
University scheduled written examination	Online	65%	120 minutes
Other		0%
Total		100%	120 minutes

• Pass standard: 40%
• Could the result of this module be capped? No
• Penalties: Work submitted after the deadline will not be graded and will not count.

• Are supplemental/resit registrations permitted? Yes
• Continuous assessment mark is carried forward to the Autumn
• University scheduled written examination (Autumn): 120 minutes, Online

• DS151

For a full timetable search, go to NUIM Timetable. Venue locations can be found here.