NCHNAP562 Linear Algebra and Probability for Data Science Course Descriptor

Course Summary

This course offers an introduction to the basics of statistics, probability, and linear algebra. It covers random variables, frequency distributions, measures of central tendency, measures of dispersion, moments of a distribution, discrete and continuous probability distributions, chain rule, Bayes’ rule, correlation theory, basic sampling, matrix operations, trace of a matrix, norms, linear independence and ranks, inverse of a matrix, orthogonal matrices, range and null-space of a matrix, the determinant of a matrix, positive semidefinite matrices, eigenvalues, and eigenvectors.

Course Aims

• Train learners in the key concepts of linear algebra, calculus, statistics and probability and the tools available to solve them.
• Train learners in the study of vectors, matrices and determinants and their role in solving a wide range of data science related problems.
• To give learners the theoretical background and tools to engage in the analysis of numerical data, apply mathematical methods and identify optimal solutions.

Learning Outcomes

On successful completion of the course, learners will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• On-line learning
• On-line discussion groups
• On-line assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Assessment will be in two forms:

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.

Feedback is provided on summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books

• Strang, G., (2019), Introduction to Linear Algebra, Wellesley, Mass.: Wellesley-Cambridge
• Graham, R., Knuth, D. and Patashnik, O., (1994), Concrete Mathematics: A Foundation for Computer Science, Reading, Mass.; Wokingham: Addison-Wesley
• Attword, G., Dyer, G., and Skipworth, G., (2014), Statistics, New York: McGraw-Hill Education

Journals

Learners are encouraged to consult relevant journals on linear algebra, statistics and probability. 

Electronic Resources

Learners are encouraged to consult relevant electronic resources on linear algebra, statistics and probability. 

Indicative Topics

• Linear algebra and transformations
• Vectors and matrices
• Statistics and Probability