15 Credits SPRING



Aims/Description: As the volume of and types of information collected and stored in databases grows, there is a growing need to gain new insights into the data by identifying important patterns and trends. Data Mining is the process by which this is done. This module will examine the two main goals served by data mining: (i) insight (identifying patterns and trends on which to base actions), and (ii) prediction (modelling future activities or outcomes based on input data) and how algorithms are used to support these. An overview will be provided of the algorithms that underpin the most commonly used machine learning methods for building models and identifying patterns in data.

Staff Contact: Prof Val Gillet
Teaching Methods: Lectures, Problem solving, Laboratory work, Independent Study
Assessment: Course work

Information on the department responsible for this unit (Information School):

Departmental Home Page
Teaching timetable

|

NOTE
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

URLs used in these pages are subject to year-on-year change. For this reason we recommend that you do not bookmark these pages or set them as favourites.

Western Bank, Sheffield, S10 2TN, UK