Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Advanced Data Science | |||
| Reference | CMM536 | Version | 7 |
| Created | June 2022 | SCQF Level | SCQF 11 |
| Approved | April 2015 | SCQF Points | 15 |
| Amended | July 2022 | ECTS Points | 7.5 |
| Aims of Module | |||
|---|---|---|---|
| To introduce students to complex datasets, showing how processing and analysis techniques can be adapted to address the challenges posed by the nature of such data in real-life applications. | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Critically appraise the challenges posed by the management and processing of complex datasets and data inputs. |
| 2 | Discuss, compare and contrast advanced techniques and algorithms for working with complex datasets and data types using data science. |
| 3 | Critically evaluate and select state-of-the-art data science techniques and algorithms for selected/given applications involving complex data. |
| 4 | Apply advanced techniques and algorithms and critically analyse and evaluate the results. |
| Indicative Module Content |
|---|
| Introduction to Python (including use of IDEs, Environments and modules such as Numpy, Pandas, Keras, Tensorflow/TensorBoard). Complex Data: Image data (Pixel grid, colour channels, feature extraction); Streaming data (concept drift, streaming rate, class imbalance). Data pre-processing. Classification: Convolutional Neural Networks for Images; Streaming Data Classification. |
| Module Delivery |
|---|
| This is a lecture-based module with practical exercises that will feature a number of advanced data mining techniques as applied to complex datasets high-speed data streams. Online materials and online sessions will be used to support online learning students. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 30 | 30 |
| Non-Contact Hours | 120 | 120 |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 150 | 150 |
| 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: | A coursework assessing 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 this 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 | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | AGGARWAL, C. C. (2007). Data streams: models and algorithms (Vol. 31). Springer. |
| 2 | GAMA, J. and GABER, M. M. (2007). Learning from data streams. Springer-Verlag Berlin Heidelberg. |
| 3 | Python, Toby Donaldson, Peachpit Press (2013) |
| 4 | Python Essentials. Steven F. Lott, Packt Publishing Ltd(2015) |
| 5 | Think Python: How to Think Like a Computer Scientist. Allen Downey, O'Reilly Media, Inc. (2012) |
| 6 | Fluent Python. Luciano Ramalho. O'Reilly Media, Inc. (2015) |
| 7 | Python Cookbook: Recipes for Mastering Python 3. David Beazley, Brian K. Jones, O'Reilly Media, Inc. (2013) |
| 8 | Deep Learning with Python. Francois Chollet. Manning. (2018) |