161762

Multivariate Analysis for Big Data

Research methods suitable for the analysis of big datasets containing many variables. The fundamentals of data visualisation, customer segmentation, factor analysis and latent class analysis with examples taken from business and health fields. Emphasis will be placed on achieving a conceptual understanding of the methods in order to implement and interpret the outcomes of multivariate analyses.

Course code

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

161762

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

Subject

Statistics

Course planning information

Course notes

Analysis in this course is supported primarily by SAS software. Access to a Windows PC is required for analysis of data.

Students must attend block courses.

Restrictions

Similar content
161323, 161772

You cannot enrol in this course if you have passed (or are enrolled in) any of the course(s) above as these courses have similar content or content at a higher level.

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 Explain the properties of multivariate data and distinguish a multivariate analysis from a univariate analysis in a research setting.
  • 2 Explain the problems and opportunities that big data bring to standard analytical methods.
  • 3 Explain and demonstrate how to use methods of cluster analysis and ordination of multivariate data, including principal components analysis (PCA), factor analysis, or multi-dimensional scaling (MDS), by virtue of their conceptual properties, and using statistical software.
  • 4 Explain and demonstrate how to perform latent variable analyses using statistical software.
  • 5 Perform analyses using statistical software to discriminate a priori groups using multivariate data, and to assess classification error of such models.
  • 6 Apply and interpret appropriate cluster analysis, ordination methods and/or latent variable methods to analyse new multivariate data and write up the results in a structured, statistically sound research report.

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

Assessments

Assessment Learning outcomes assessed Weighting
Computer programmes 1 2 10%
Written Assignment 1 2 15%
Computer programmes 3 4 5 10%
Written Assignment 3 4 5 15%
Written Assignment 1 2 3 4 5 6 50%

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.

Textbooks needed

There are no set texts for this course.