Courses / Module

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

• Acquire familiarity with the basic concepts of data science and the importance of data science, in society, and in business;
• Be able to distinguish between different types of data;
• Be introduced to the concepts of big data and machine learning for identifying patterns in data;
• Learn to identify challenges related to big data;
• Acquire data visualisation and analysis skills through using the R programming language;
• Gain an understanding how data modelling can be used for prediction;
• Gain experience with writing analysis reports.

• 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

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

 

 

 

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