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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Application Domains 2
Module Code CA4016
School School of Computing
Module Co-ordinatorSemester 1: Andrew Way
Semester 2: Andrew Way
Autumn: Andrew Way
Module TeachersAndrew Way
Cathal Gurrin
NFQ level 8 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Coursework Only
The continuous assessment course work may be re-taken.

This module presents students with a series of four domains in which data analytics have had, or are having, a transformative effect on our lives. Students will emerge with a familiarity and an understanding of how data analytics, visualisation, and other aspects of data science are being used to change the world in which we live. The four application domains covered in this module are Smart Planning (how various data sources, including open data, are now used in planning the world around us), Digital Humanities (how digitisation of various cultural artifacts has completely opened up a new way in which humanities teaching and research is now conducted), Language Technologies (how access to huge volumes of online information has transformed language processing, from translation to search), and Ethical AI (essentially, 'AI for Good', to be contrasted in Pods 3 where AI for more nefarious purposes will be investigated).

Learning Outcomes

1. Explain applications of data science and data analytics in 4 different domains namely Smart Planning, AI for Good, Language Technologies, and Digital Humanities.
2. Predict potential for other, data-driven approaches to major aspects of our lives in other domains
3. Summarise the main issues and challenges for data-driven approaches in the 4 domains
4. Debate the scope of data-driven approaches to major aspects of our lives in the 4 domains

Workload Full-time hours per semester
Type Hours Description
Lecture48A series of guest lectures from industry and enterprise partners in each of the 4 application domains for this module
Assignment Completion48Completion of assignments
Independent Study91In order to complete the shared task, the students will need to consult prior work on the topic, come up with novel solutions (perhaps inc. new datasets) to the problem, evaluate their efforts compared to the state of the art, familiarise themselves with how to write an academic paper in LaTeX, and present their work in class at the end of the task.
Total Workload: 187

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

Indicative Content and Learning Activities

A suite of online resources will be made available through the Loop page for the module.
During this course, a number of expert speakers will explain how and why data gathering and analysis can be of use in a range of application areas. In Pods 1, we focused on insurance, journalism, finance, and health & human performance. In Pods 2 we will focus on Machine Learning/AI applications in: (i) Smart Infrastructural Planning: Smart Cities, Smart Stadiums, Smart Planning etc.; (ii) Digital Humanities: translation, digital archiving, art etc.; (iii) AI for Good, contrasting that in Pods 3 with Unethical AI. Ultimately we want to achieve 'Driveable AI', with much more user control than heretofore; (iv) NLP systems which benefit a range of users. There will be a mix of generic online material, and guest lectures specifically commissioned for this module. Seminars will follow up on the main issues arising from the consumption of this material. Students will be confronted with a particular shared task post hoc in the area of NLP. They will receive training in the specific domain of application (e.g. Quality Estimation, Machine Translation etc), and be exposed to prior work in that area. Working in groups, they will then decide on their own post hoc participation in the task, and (I) write a functional spec describing this work, (ii) write an academic paper on what they did, and what results were obtained, and (iii) present these findings as a group as well as individually via an in-class presentation.

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Group assignmentStudents will compete a number of sub-assessments working in groups post hoc on a shared task in language technology (e.g. MT, Quality Estimation etc). This will be and end-to-end task requiring a functional spec (10%), a scientific paper (60%), and a presentation (30%).100%n/a
Reassessment Requirement Type
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
This module is category 1
Indicative Reading List

    Other Resources

    Programme or List of Programmes
    DSBSc in Data Science
    MCMECMicro-Credential Modules Eng & Comp
    SMPECSingle Module Programme (Eng & Comp)

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