Run by School of Computer Science and Electronic Engineering

10.000 Credits or 5.000 ECTS Credits

Semester 1

This module will give learners the underpinning mathematical knowledge needed for the completion of their degree. The module will provide an introduction to algorithms, discrete mathematics and statistical analysis. Discrete mathematics will examine set theory, matrices and Boolean logic. Statistical analysis will provide an introduction to probability, probability distributions and the theories involved in the interpretation of large sets of data using statistical analysis.

Indicative content includes:

• Revision of key numerical skills, such as multiples, factors and primes; powers and scientific notation; ratios; basic algebra such as linear equations and simplification.
• Introduction to how computers store numbers: binary and hexadecimal.
• Introduction to algorithms: construction of algorithms and how they are used in problem solving.
• Boolean logic: simple logic using gates, truth tables and propositional logic.
• Set theory: basic concepts; representation of set data using Venn diagrams; set operations; equivalence; cardinality.
• Matrices: Matrix manipulation and operations, networks, matrix inverses, solving simultaneous equations using matrices.
• Statistics and probability: definition of key terms, combination, permutation, presentation of statistical data, conditional probability, Bayes' theorem, expectation, variance, standard deviation.

threshold

Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

excellent

Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

good

Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

1. Understand how a computer represents numerical data types and how they are applied in Boolean logic.

2. Define simple algorithms and utilise problem solving techniques.

3. Use matrices to solve a range of problems.

4. Interpret set theory notation, perform operations on sets and reason about sets.

5. Utilise an appropriate range of techniques in the statistical analysis of sets of data.

Type	Name	Description	Weight
	Individual Problem Set 1		20.00
	Individual Problem Set 2		30.00
	Unseen Examination		50.00

		Hours
Tutorial	Tutor led seminars and tutorials.	5
Lecture	Classroom-based lectures.	20
Private study	Tutor/employer-directed student learning.	75

• Literacy - Proficiency in reading and writing through a variety of media
• Numeracy - Proficiency in using numbers at appropriate levels of accuracy
• Computer Literacy - Proficiency in using a varied range of computer software
• Self-Management - Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
• Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
• Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.

• Problem solving strategies
• Development of general transferable skills
• Knowledge and understanding of mathematical principles
• Knowledge and understanding of computational modelling

Talis Reading list

http://readinglists.bangor.ac.uk/modules/icl-1009.html

Reading list

• Biggs NL, 2003, Discrete Mathematics, 2nd ed, Oxford University Press.
• Levin O, 2018, Discrete Mathematics: An Open Introduction, 3rd ed, Independent.
• Rosen KH, 2018, Discrete Mathematics and Its Applications, 8th ed, McGraw-Hill.
• Ross KA and Wright CRB, 2012 Discrete Mathematics, 5th ed, Pearson.

Compulsory in courses:

• H116: BSc Applied Data Science (Degree Apprenticeship) year 1 (BSC/ADS)
• H120: BSc Applied Data Science (Degree Apprentice - Coleg Cambria) year 1 (BSC/ADSC)