Short Title:Project - Data Analytics Specialisation
Full Title:Project - Data Analytics Specialisation
Language of Instruction:English
Module Code:PROJ H4022
 
Credits: 10
Field of Study:Computer Science
Module Delivered in 2 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:Keith Quille
Module Description:A significant data analytics/data science project
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Work independently in the development of significant data analytics/data science project
LO2 Design and implement a data science model as part of a data analytics project, the model being used to extract useful information and/or make predictions using the data for a specific context
LO3 Use one or combinations of; state the art data retrieval, extraction, transformation, vision, image, language, sound, text, game, data analysis, data mining, machine learning, artificial intelligence or deep learning techniques as part of a data analytics project
LO4 Plan a project, implement it and evaluate it (using state of the art statistical techniques where appropriate)
LO5 Present the project to peers in a formal manor
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Design, implement and refine a data science model (or equivalent system) as part of a data analytics project. The project must use an appropriate model to solve or address a specific problem, and report on the findings and/or model performance. The model will be developed and refined as part of the implementation. Students will be encouraged to work independently and show initiative. Students will be required to plan and manage their project in conjunction with a supervisor. Students will be required to present their projects and findings to staff. Deliverables - model, research report, summarized research document and a presentation on the projects findings. 1,2,3,4,5 100.00 Ongoing
No End of Module Formal Examination

TU Dublin – Tallaght Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecturer Supervised Learning Lecturer Supervised Learning 1.00 Every Week 1.00
Independent Learning Independent Learning 11.00 Every Week 11.00
Total Weekly Learner Workload 12.00
Total Weekly Contact Hours 1.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecturer Supervised Learning Lecturer Supervised Learning 1.00 Every Week 1.00
Independent Learning Time Independent Learning Time 11.00 Every Week 11.00
Total Weekly Learner Workload 12.00
Total Weekly Contact Hours 1.00
 

Module Resources

Required Book Resources
  • Ian H Witten, Eibe Frank, Mark a. Hall, Christopher J. Pal 2017, Data Mining: Practical Machine Learning Tools and Techniques, Fourth Ed. Ed., Morgan Kaufmann Series in Data Management systems [ISBN: 978047090874]
  • Myatt and Johnson 2006, Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining, Wiley [ISBN: 047007471X]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KACOD_B Bachelor of Science (Hons) in Computing with Data Analytics 8 Mandatory
TA_KCODA_B Bachelor of Science (Hons) in Computing with Data Analytics (Add-On) 2 Mandatory