Registry
Module Specifications
Archived Version 2021 - 2022
| Module Title |
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| Module Code |
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School |
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Online Module Resources
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NFQ level |
9 |
Credit Rating |
7.5
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Pre-requisite |
None
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Co-requisite |
None
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| Compatibles |
None
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| Incompatibles |
None
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Description
The rapid rise of new ways of thinking about finance, as well as technological progress, has created a necessity for finance professionals to be able to work with new forms of data. This move echoes the wider rise of data science as an approach to business. This module will cover the nature and approaches of data science and, in specific, will approach finance through an immersion in Python coding. Python is the most popular language of data science and this module will bring you from the very basics of coding in Python through to advanced techniques for understanding financial data.
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Learning Outcomes
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| Workload |
Full-time hours per semester |
| Type |
Hours |
Description |
| Lecture | 12 | Formal classes (lecture / workshop), one hour per week delivered live online, with a recorded option available. | | Lecture | 12 | Formal classes (lecture), one hour per week delivered in pre-recorded online learning | | Assignment Completion | 76 | Two individual assignments and a group presentation. | | Independent Study | 87.5 | Class related reading and activities, guided reading and further learning. | | Total Workload: 187.5 |
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml
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Indicative Content and
Learning Activities
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| Assessment Breakdown | | Continuous Assessment | % | Examination Weight | % |
| Course Work Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Reassessment Requirement |
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component |
| Unavailable |
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Indicative Reading List
- Brooks, Chris: 2019, Python Guide for Introductory Econometrics for Finance, Cambridge University Press,
- Brooks, Chris: 2019, Introductory Econometrics for Finance, Cambridge University Press, 978-110842253
- Wes McKinney: 2017, Python for Data Analysis, O'Reilly Media, 1491957662
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Other Resources
45239, 0, DataCamp training (various courses), www.datacamp.com,
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Programme or List of Programmes
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| Archives: | |
