Current Academic Year 2024 - 2025
All Module information is indicative, and this portal is an interim interface pending the full upgrade of Coursebuilder and subsequent integration to the new DCU Student Information System (DCU Key).
As such, this is a point in time view of data which will be refreshed periodically. Some fields/data may not yet be available pending the completion of the full Coursebuilder upgrade and integration project. We will post status updates as they become available. Thank you for your patience and understanding.
Date posted: September 2024
| Module Title |
Professional & Research Practice for Data Science |
| Module Code |
CA375 (ITS) / CSC1095 (Banner) |
|
Faculty |
Engineering & Computing |
School |
Computing |
| Module Co-ordinator | Gareth Jones | | | Module Teachers | - | |
|
NFQ level |
8 |
Credit Rating |
5 |
|
Pre-requisite |
Not Available |
|
Co-requisite |
Not Available |
| Compatibles |
Not Available |
| Incompatibles |
Not Available |
|
Repeat examination
|
Description
The aim of this module is for the student to understand and manage the Social, Legal, Ethical and Regulatory issues and dilemmas associated with professional work in a Data Science environment. This includes the philosophy of research, qualitative and quantitative research, accessing and evaluating research materials, assessing outcomes and dissemination. It will examine the specific issues of experimental design relevant to data science.
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Learning Outcomes
1. Discriminate between the key social, legal, ethical and regulatory issues involved in working professionally in computing.
2. Critically analyse the detail and implications of key Irish and European legislation with respect to data, data storage and data analysis.
3. Construct approaches to managing within legal, ethical and regulatory parameters.
4. Describe the vocabulary and concepts of both social, legal, ethical and regulatory approaches and be prepared to deal with workplace issues on the basis of this understanding.
5. Develop a philosophical and legal outlook that will be transferable to academic study and work generally.
6. Describe and deploy the concepts of research and novel investigation.
7. Design experiments for data based investigations in data science.
|
| Workload |
Full-time hours per semester |
| Type |
Hours |
Description |
| Lecture | 24 | Course content, including supplementary material on key topics and associated tutorial material will be made available online using the DCU Loop e-learning system and other appropriate electronic means. | | Independent Study | 101 | This comprises time for reading, reviewing given and other exercises, group interaction on project, project time and write-up and revision. | | Total Workload: 125 |
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
|
| Assessment Breakdown | | Continuous Assessment | 100% | Examination Weight | 0% |
| Course Work Breakdown |
| Type | Description | % of total | Assessment Date |
|---|
| Extended Essay / Dissertation | Review of selected topic in use of practical investigations in data science, reflecting on the potential legal, ethical, societal and regulatory implications of the work. | 20% | n/a | | Group assignment | Proposal and plan for data science experimental
investigation. | 20% | n/a | | Group presentation | Presentation corresponding to experimental design in written report. | 10% | n/a | | Report(s) | Series of scenario based short report assignments applying research methods introduced in the module. | 25% | n/a | | Report(s) | Complete series of assignments considering the application of ethical and legal principles for data scientists. | 25% | n/a |
| Reassessment Requirement Type |
Resit arrangements are explained by the following categories:
Resit category 1: A resit is available for both* components of the module.
Resit category 2: No resit is available for a 100% continuous assessment module.
Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element. |
| * ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment |
| This module is category 1 |
|
Indicative Reading List
- John Ladley: 0, Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, Morgan Kaufmann, 0124158293
- Alex Berson, Larry Dubov: 0, MASTER DATA MANAGEMENT AND DATA GOVERNANCE, 2/E, McGraw-Hill Osborne Media, 0071744584
- Alan Calder, Steve Watkins: 2015, IT Governance: An International Guide to Data Security and ISO27001/ISO27002 Paperback, 978074947405
- David Sutton: 2014, Information Risk Management: A Practitioner's Guide, 1780172656
- Lothar Determann: 2016, Determann's Field Guide to Data Privacy Law: International Corporate Compliance, 1783476885
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Other Resources
None |
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