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| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Computational Mathematics | |||
| Reference | CM1606 | Version | 1 |
| Created | March 2020 | SCQF Level | SCQF 7 |
| Approved | July 2020 | SCQF Points | 30 |
| Amended | ECTS Points | 15 | |
| Aims of Module | |||
|---|---|---|---|
| To provide background knowledge in mathematical concepts, terminology and notations required for problem solving in artificial intelligence (AI) and data science (DS). | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Demonstrate the knowledge associated with the mathematical theorems for AI and DS; using notations and proofs to formulate AI and DS related problems. |
| 2 | Formulate loss/cost/objective functions and critically analyse their minimisation/maximisation properties. |
| 3 | Apply a range of statistical distribution models and hypothesis testing to real-world problems. |
| 4 | Represent, analyse and visualise data, in order to infer helpful insights about data collections. |
| Indicative Module Content |
|---|
| Logic, Set Theory, Relations, Functions, Logarithms, Probability, Modular Arithmetic, Matrices, Calculus and Linear Algebra: Vectors, Tensor Operations, Data Analysis, Conditional Probability, Discrete Distributions, Continuous Probability Distributions, Inferential Statistics (Linear Regression, Normal Distribution, Binominal Distribution), Hypothesis Testing. Analysis of Variance. Maths and statistics coding platforms; Python and R Studio. |
| Module Delivery |
|---|
| The module will be delivered through a mixture of lectures and tutorial sessions. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 96 | N/A |
| Non-Contact Hours | 204 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 300 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Examination | Weighting: | 40% | Outcomes Assessed: | 1, 2 |
| Description: | Examination on maths for data science. | ||||
| Component 2 | |||||
| Type: | Coursework | Weighting: | 60% | Outcomes Assessed: | 3, 4 |
| Description: | Coursework assignment for statistical data analytics. | ||||
| MODULE PERFORMANCE DESCRIPTOR | ||||||||
|---|---|---|---|---|---|---|---|---|
| Explanatory Text | ||||||||
| The calculation of the overall grade for this module is based on a 60% weighting for Component 1 (Examination) and 40% weighting for Component 2 (Coursework). An overall minimum grade D is required to pass the module. | ||||||||
| Coursework: | ||||||||
| Examination: | A | B | C | D | E | F | NS | |
| A | A | A | B | B | C | E | ||
| B | B | B | B | C | C | E | ||
| C | B | C | C | C | D | E | ||
| D | C | C | D | D | D | E | ||
| E | C | D | D | E | E | E | ||
| F | E | E | E | E | F | F | ||
| NS | Non-submission of work by published deadline or non-attendance for examination | |||||||
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Stroud, KA. 2009. Foundation Mathematics. Palgrave MacMillan. |
| 2 | Bruce, A. and Bruce, P. 2020. Practical Statistics for Data Scientists. O'Reilly. |
| 3 | Grossman, P. 2008. Discrete Mathematics for Computing. Palgrave MacMillan. |
| 4 | Strang, G. 2016. Introduction to Linear Algebra. 5th ed. Wellesley-Cambridge Press. |
| 5 | Lipschutz, S. 1998. Schaum's Outline of Set Theory and Related. 2nd ed. McGraw-Hill Education. |