Module Details

Module Code: DATA C9006
Full Title: Ethics in Data Analytics
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 5
Module Owner:: Rajesh Jaiswal
Departments: Unknown
Module Description: This module aims to provide an understanding of the moral and ethical considerations in Data Analytics life cycle. The module provides framework to analyze ethical concerns related to collecting, analyzing and managing big-data. Completing this module will enable students to comprehend the ethical, social and legal implications related to the data ownership, privacy, security and use with informed consent.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Critic about morality and etiquette, law, and professional codes of conduct
MLO2 Recognize and classify ethical issues that arise in Data Analytics
MLO3 Construct an ethical argument, recognize fallacies, and debate ethical trade-off rationally based on the data analytics framework
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named DkIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
 
Module Indicative Content
Introduction
History of ethics in Computing, Growth of big data, Development of ethical issues
Ethics and Ethical Analysis
Ethical theories, ethical reasoning and decision making, and codes of ethics, ACM Code of Ethics and Professional Conduct
Ethical and legal framework for Data Analytics life cycle
Digital divide, Anonymity, Privacy, Security, GDPR, Ownership, Consent and Data Governance
Ethical issues
Case studies related to Data analytics, qualitative and/or quantitative analysis
Module Assessment
Assessment Breakdown%
Course Work50.00%
Final Examination50.00%
Module Special Regulation
 

Assessments

Full-time

Course Work
Assessment Type Presentation % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing Every Week Learning Outcome 2,3
Duration in minutes 0
Assessment Description
CA1- one debate every week discussing the case studies related to ethics in data analytics
Assessment Type Written Report % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing S1 Week 8 Learning Outcome 1,2,3
Duration in minutes 0
Assessment Description
CA2 - One proposal of recent ethical issue in the field of Data Analytics and the corresponding written report containing ethical, social and legal implications
No Project
No Practical
Final Examination
Assessment Type Formal Exam % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3
Duration in minutes 120
Assessment Description
End of Module Examination covering all the learning outcomes

Part-time

Course Work
Assessment Type Presentation % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing Every Week Learning Outcome 2,3
Duration in minutes 0
Assessment Description
CA1- one debate every week discussing the case studies related to ethics in data analytics
Assessment Type Written Report % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing S1 Week 8 Learning Outcome 1,2,3
Duration in minutes 0
Assessment Description
CA2 - One proposal of recent ethical issue in the field of Data Analytics and the corresponding written report containing ethical, social and legal implications
No Project
No Practical
Final Examination
Assessment Type Formal Exam % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3
Duration in minutes 120
Assessment Description
End of Module Examination covering all the learning outcomes
Reassessment Requirement
A repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full-time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact To cover theory of ethics in Data Analytics Every Week 1.00 1
Lecturer-Supervised Learning (Contact) Contact Debates on ethical issues Every Week 1.00 1
Directed Reading Non Contact Lecture notes, books and online materials Every Week 2.00 2
Independent Study Non Contact Lecture notes, books and online materials Every Week 4.00 4
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 2.00
Workload: Part-time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact To cover theory of ethics in Data Analytics Every Week 1.00 1
Lecturer-Supervised Learning (Contact) Contact Debates on ethical issues Every Week 1.00 1
Directed Reading Non Contact Lecture notes, books and online materials Every Week 2.00 2
Independent Study Non Contact Lecture notes, books and online materials Every Week 4.00 4
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 2.00
 
Module Resources
Recommended Book Resources
  • (2013), Ethical and Social Issues in the Information Age, 5th Edition. Springer, [ISBN: 971447159728].
  • Michael J Quinn. (2015), Ethics for the Information Age, 6th Edition. Pearson.
  • O'Neill, Kathy. (2017), Weapons of Math Destruction, 1st. Penguin.
Recommended Article/Paper Resources
Other Resources