MS1S461 - Mathematics and Statistics for Computing 01 Jul 2022 - 31 Aug 2028 | Version 3
Associated Module Information
| Module Code: | MS1S461 | ||
|---|---|---|---|
| Module Title: | Mathematics and Statistics for Computing | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Maths | ||
| Module Leader: | Graeme Boswell | ||
| Module Team: | Stephanie Perkins, Paul Messenger, Nicolas Andrews, Adam Jones | ||
| First Intended Intake: | Final Year of Intake: | ||
| Date Closed: | |||
| Credit Value: | 20 | Credit Level: | 4 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100403 - mathematics | 100406 - statistics | |
| HECOS Code Weighting: | 50 | 50 | |
Document Version Information
| Version | 3 |
|---|---|
| Valid From | 01 Jul 2022 |
| Valid To | 31 Aug 2028 |
Module Aims
To provide a knowledge of basic mathematical and statistical concepts in order to underpin the work of parallel and succeeding modules throughout the course.
To enable students to solve problems and appreciate differences in problem solving techniques.
To enable students to apply the theory to problems in computing and understand the limitations of the solutions found.
Content Summary
Number bases: binary and hexadecimal.
Series; sigma notation, arithmetic and geometric progressions.
Logic: implication, equivalence, truth tables, De Morgan’s laws.
Sets: basic concepts, power sets, set operations, basic laws, number sets.
Introduction to Number Theory: Properties of number, integers, primes.
Statistics: Presentation of data, measures of location and dispersion, cumulative frequency, inter-quartile range. Regression and correlation. T-test for difference in mean independent samples, and paired data and chi squared tests.
Probability: Introduction to probability theory, normal, binomial and Poisson distributions.
Introduction to decision theory: expected value criterion; utility functions; decision trees; Laplace criterion; minimax criterion.
Risk: Definitions (generic vs security risk), quantitative risk, risk leverage, decision trees.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 24 |
| Practical classes and workshops | 24 |
| Independent Study | 80 |
| Directed Study | 72 |
| Total Hours Selected | 200 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Apply a range of mathematical and statistical problem solving techniques, and interpret the solutions appropriately. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Practical Coursework 1 (Asynch) | Coursework | 0 | N/A | 50 | No | 40 |
| Synchronous Onsite Assessment | Classroom Test - Time Constrained (Onsite) 1 | Test | 55 | N/A | 50 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | |||
| Practical Coursework 1 (Asynch) | ✔ | ||
| Classroom Test - Time Constrained (Onsite) 1 | ✔ | ||