Courses / Module

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

• Understand the life cycle of a data science project and the role of the data scientist within that.
• Collate and manipulate data in software such as SAS or R.
• Identify tools to handle the breadth of challenges that can arise within a data science project, such as missing data or time series data or complex data needing visualisation.

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

Delivery methods	Hours
Lectures	24
Labs / Practicals	4
Tutorials	0
Planned learning activities	0
Independent student activities	97
Total	125

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

Assessment type	Options	Weighting	Duration
Continuous Assessment		50%
University scheduled written examination	On-campus	50%	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
• University scheduled written examination (Autumn): 120 minutes, On-campus

• ST303

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