Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Advanced Mathematics for Data Science | |||
| Reference | CM2607 | Version | 3 |
| Created | February 2024 | SCQF Level | SCQF 8 |
| Approved | July 2020 | SCQF Points | 15 |
| Amended | April 2024 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce advanced mathematical concepts and functions together with illustrative artificial intelligence (AI) and data science (DS) applications. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Practice advanced mathematical ideas clearly in written form, and place them within the context of their history and applications to Artificial Intelligence (AI) and Data Science (DS). |
| 2 | Use differentiation and integration equations to solve problems in AI and DS. |
| 3 | Use theories of sequences and series to identify bounded functions. |
| 4 | Convert mathematical functions into programmable code. |
| Indicative Module Content |
|---|
| Differentiation: Single variable functions, Differentiation, Definitions only for basic functions, rules and properties, Composition of functions and partial derivatives, Higher order derivatives. Integration: Definition and Rules with properties, partial fractions and definite and indefinite integrations Application: Finding area under the curves. Sequences and Series: Definition and examples of sequences, Series and the sequence of terms of a series, Arithmetic progressions, Geometric progressions, The sum to infinity of a series and the convergence and divergence of series. Discrete cosine transform and fourier transform family. |
| Module Delivery |
|---|
| The module will be delivered through a series of lectures and tutorial sessions. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 48 | N/A |
| Non-Contact Hours | 102 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 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: | Coursework | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4 |
| Description: | Individual coursework covering all learning outcomes. | ||||
| MODULE PERFORMANCE DESCRIPTOR | |
|---|---|
| Explanatory Text | |
| The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade of D is required to pass the module. | |
| Module Grade | Minimum Requirements to achieve Module Grade: |
| A | The student needs to achieve an A in C1. |
| B | The student needs to achieve a B in C1. |
| C | The student needs to achieve a C in C1. |
| D | The student needs to achieve a D in C1. |
| E | The student needs to achieve an E in C1. |
| F | The student needs to achieve an F in C1. |
| NS | Non-submission of work by published deadline or non-attendance for examination |
| Module Requirements | |
|---|---|
| Prerequisites for Module | CM1606 or equivalent. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Stroud,K.A. and Booth, D.J. 2013. Engineering mathematics. 7th ed. Palgrave Macmillan. |
| 2 | Dass, H.K. 2008. Advanced Engineering Mathematics. S. Chand Publishing |
| 3 | Kreyszig, E. 2011. Advanced Engineering Mathematics. 10th ed. J Wiley. |
| 4 | Hamilton, D. 2018. Calculus 1 - Differentiation and Integration. Hamilton Education Guides. |
| 5 | McMullen, C. 2018. Essential Calculus Skills Practice Workbook with Full Solutions. Zishka Publishing. |
| 6 | Jay Dawani,2020, Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks, Packt Publishing |