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  6. MA2511: Applied Linear Algebra

MA2511: APPLIED LINEAR ALGEBRA (2025-2026)

Last modified: 13 Nov 2025 12:46


Course Overview

This course introduces key concepts of linear algebra, focusing on practical applications in data science. Students will explore vector spaces, matrices, eigenvalues, and linear transformations, using computational tools to demonstrate applications in optimization and data analysis. In-class practicals will involve programming in R to reinforce concepts and methods.

Course Details

Study Type Undergraduate Level 2
Term Second Term Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Juliano Morimoto

What courses & programmes must have been taken before this course?

  • Any Undergraduate Programme (Studied)
  • One of Programme Level 2 or Programme Level 3 or Programme Level 4 or Programme Level 5

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

Course Description: Applied Linear Algebra

This course offers a foundational understanding of linear algebra and its critical applications within the field of data science. Designed for second-year students with some prior knowledge of linear algebra concepts, this course serves as a bridge to deepen understanding in practical contexts.

By the end of this course, students will be able to:

  1. Understand Fundamental Concepts: Demonstrate knowledge of vectors, matrices, and their properties.
  2. Solve Linear Systems: Apply techniques for solving linear equations, including Gaussian elimination and matrix inversion.
  3. Explore Vector Spaces: Grasp the concepts of vector spaces and subspaces, including basis and dimension.
  4. Understand Eigenvalues and Eigenvectors: Identify and compute eigenvalues and eigenvectors and understand their significance in data transformations.
  5. Utilize Linear Transformations: Explain linear transformations and their matrix representations in various contexts.
  6. Demonstrate Applications: Use programming software (such as R) to demonstrate real-world applications of linear algebra in data analysis and optimization tasks.

While there are no formal requirements to taking this course, we strongly encourage students to be familiar (although not necessarily proficient) with quantitative subjects.


Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2025 for 1st Term courses and 19 December 2025 for 2nd Term courses.

Summative Assessments

Computer Programming Exercise

Assessment Type Summative Weighting 60
Assessment Weeks 40 Feedback Weeks 42

Look up Week Numbers

Feedback

(5 x A4 pages) 

Written feedback will be provided and students may seek further feedback from Course Co-ordinator individually. Use of generative AI is permitted, and the task is designed to accommodate this while maintaining an element of critical thinking and interpretation that GenAI cannot provide.
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand key linear algebra concepts, including vectors, matrices, and vector spaces, and their relevance to data science applications.
ProceduralAnalyseAnalyse data sets using R, demonstrating the application of linear algebra methods in data manipulation and analysis.
ProceduralApplyUse techniques to solve systems of linear equations in practical scenarios.

Computer Lab Reports (5)

Assessment Type Summative Weighting 20
Assessment Weeks 28,30,32,34,39 Feedback Weeks 29,31,33,35,39

Look up Week Numbers

Feedback

5 x 2 x A4 page Computer Lab Reports 

Written feedback will be provided and students may seek further feedback from tutors individually.
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand key linear algebra concepts, including vectors, matrices, and vector spaces, and their relevance to data science applications.
ProceduralAnalyseAnalyse data sets using R, demonstrating the application of linear algebra methods in data manipulation and analysis.
ProceduralApplyUse techniques to solve systems of linear equations in practical scenarios.

Online Quizzes (3)

Assessment Type Summative Weighting 20
Assessment Weeks 29,34,38 Feedback Weeks 29,34,38

Look up Week Numbers

Feedback

Written feedback will be provided and students may seek further feedback from tutors individually.

Duration: 1 hour
Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandUnderstand key linear algebra concepts, including vectors, matrices, and vector spaces, and their relevance to data science applications.
ProceduralAnalyseAnalyse data sets using R, demonstrating the application of linear algebra methods in data manipulation and analysis.
ProceduralApplyUse techniques to solve systems of linear equations in practical scenarios.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Feedback

Exam (2 hours). Best of (resit exam mark) or (resit exam mark with carried forward CA marks).
Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralApplyUse techniques to solve systems of linear equations in practical scenarios.
ProceduralAnalyseAnalyse data sets using R, demonstrating the application of linear algebra methods in data manipulation and analysis.
ConceptualUnderstandUnderstand key linear algebra concepts, including vectors, matrices, and vector spaces, and their relevance to data science applications.
 

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