Module Details
Module Code: |
DATA |
Module Title:
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Data Science and Machine Learning 1
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Title:
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Data Science and Machine Learning 1
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Module Level:: |
8 |
Module Coordinator: |
Nigel Whyte
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Module Author:: |
Greg Doyle
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Module Description: |
The aim of this module is to provide students with a comprehensive understanding of and ability to evaluate and utilise data science tools and techniques ethically and legally in organisations from a software engineering perspective.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Understand, evaluate and communicate key principles, theories and techniques (particularly software engineering technologies) with respect to data, data technologies and data infrastructure in organisations from a software engineering perspective. |
LO2 |
Understand, evaluate, communicate and apply key principles, theories and techniques (particularly software engineering technologies) with respect to data analytics and related introductory machine learning techniques in organisations from a software engineering perspective. |
LO3 |
Understand, evaluate and communicate the key principles, theories and techniques behind ethics, data and legal standards as they relate to data science from a software engineering perspective. |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
Data
1. Types of Data - structured (e.g. relational), unstructured (text), semi-structured data (XML, JSON), qualitative and quantitative data, types of data, numeric, textual, mixed, qualitative, quantitative etc.
2. Data Modelling and Data Curation
Conceptual, logical, physical modelling, ER diagrams, semantic modelling, etc. management of data, data lifecycle, curation for data discovery, retrieval, maintenance of quality, ensuring data correctness and value, allow for re-use.
3. Data Preparation (data sets and data relations)
Planning, data collection/storage (structured and unstructured data), feature generation, data selection, Data Cleaning - filtering, completion, correction, standardisation/merging, transformation,
4. Data Post-processing - interpretation, documentation, evaluation.
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Data Infrastructure
1. General data infrastructure considerations
Data warehouses, databases (SQL, NoSQL, etc.), cloud infrastructures
2. Hadoop, MapReduce and alternatives
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The Data Science Process
1. Data Science/Data Analytics Process
Data science process models such as ASUM-DM, CRISP-DM, SEMMA, MTDSP, etc.
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Introduction to AI, Machine Learning and Deep Learning
1. What are AI, ML, DL
2. Representations and software tools, techniques and technologies and representations used in ML and DL
3.. Generalised linear models - linear, multiple, logistic regression
3. Introduction to supervised, unsupervised, semi-supervised, reinforcement learning etc.
4. Training, dev and test sets etc.
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Standards and Ethics
1. Ethics
Standards for and legal requirements for ethical use of data
2. Data Standards and Legal Matters
Data Protection (in particular Ireland and EU)
Freedom of Information (in particular Ireland and EU)
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Module Content & Assessment
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Assessment Breakdown | % |
Project | 60.00% |
End of Module Formal Examination | 40.00% |
AssessmentsFull Time
End of Module Formal Examination |
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Reassessment Requirement |
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.
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Reassessment Description Typically, learners are reassessed via a repeat of the End of Module Formal Examination
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SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
Workload: Full Time |
Workload Type |
Workload Category |
Contact Type |
Workload Description |
Frequency |
Average Weekly Learner Workload |
Hours |
Lecture |
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Contact |
Lecture |
12 Weeks per Stage |
2.00 |
24 |
Estimated Learner Hours |
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Non Contact |
Estimate Learner Hours |
15 Weeks per Stage |
5.13 |
77 |
Laboratory |
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Contact |
Laboratory |
12 Weeks per Stage |
2.00 |
24 |
Total Weekly Contact Hours |
4.00 |
Module Resources
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Supplementary Book Resources |
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Grus J.. (2019), Data Science from Scratch: First Principles with Python, Second. All, O'Reilly, [ISBN: ISBN: 9781492].
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Andreas C. Müller,Sarah Guido. (2019), Introduction to Machine Learning with Python, First edition - Fourth relaease. O'Reilly Media, p.376, [ISBN: 9781449369415].
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Garrett Grolemund,Hadley Wickham. R for Data Science, First edition - Fourth release. [ISBN: 9781491910399].
| This module does not have any article/paper resources |
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This module does not have any other resources |
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