161777

Practical Data Mining

A practical approach to data mining with large volumes of complex data; prepare, cleanse and explore data; supervised and unsupervised modelling with association rules and market basket analysis, decision trees, multi-layer neural networks, k-nearest neighbours, k-means clustering and self-organising maps, ensemble and bundling techniques, text mining; use of leading software tools; business examples and research literature.

Course code

Qualifications are made up of courses. Some universities call these papers. Each course is numbered using six digits.

161777

Level

The fourth number of the course code shows the level of the course. For example, in course 219206, the fourth number is a 2, so it is a 200-level course (usually studied in the second year of full-time study).

700-level

Credits

Each course is worth a number of credits. You combine courses (credits) to meet the total number of credits needed for your qualification.

15

Course planning information

Course notes

Access to a Windows PC is required for analysis of data.

Restrictions

Choose just one

The courses listed above have similar content to this one meaning you can only enrol in this course or one of the listed courses. Only one of the courses can be credited towards your qualification.

General progression requirements

You may enrol in a postgraduate course (that is a 700-, 800- or 900-level course) if you meet the prerequisites for that course and have been admitted to a qualification which lists the course in its schedule.

Learning outcomes

What you will learn. Knowledge, skills and attitudes you’ll be able to show as a result of successfully finishing this course.

  • 1 Compare several models and select and interpret the most appropriate for the data.
  • 2 Use a software package such as SAS EM for data mining purposes.
  • 3 Apply appropriate Data Mining techniques to analyse data.
  • 4 Critically evaluate a technical report.

Learning outcomes can change before the start of the semester you are studying the course in.

Assessments

Assessment Learning outcomes assessed Weighting
Written Assignment 1 2 4 20%
Written Assignment 1 2 4 20%
Written Assignment 2 3 20%
Written Assignment 1 2 3 4 40%

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

Explanation of assessment types

Computer programmes
Computer animation and screening, design, programming, models and other computer work.
Creative compositions
Animations, films, models, textiles, websites, and other compositions.
Exam College or GRS-based (not centrally scheduled)
An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.
Exam (centrally scheduled)
An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.
Oral or performance or presentation
Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.
Participation
You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.
Portfolio
Creative, learning, online, narrative, photographic, written, and other portfolios.
Practical or placement
Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.
Simulation
Technology-based or experience-based simulations.
Test
Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.
Written assignment
Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.