SETU Curriculum - DATA - Data Science and Machine Learning 1

Module Details

Module Code:	DATA
Module Title:	Data Science and Machine Learning 1
Title:	Data Science and Machine Learning 1
Module Level::	8

Credits::	5

Module Coordinator:	Nigel Whyte

Module Author::	Greg Doyle

Domains:

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.

Learning Outcomes
On successful completion of this module the learner will be able to:
#	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.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed

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.
Data Infrastructure 1. General data infrastructure considerations Data warehouses, databases (SQL, NoSQL, etc.), cloud infrastructures 2. Hadoop, MapReduce and alternatives
The Data Science Process 1. Data Science/Data Analytics Process Data science process models such as ASUM-DM, CRISP-DM, SEMMA, MTDSP, etc.
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.
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)

Assessment Breakdown	%
Module Content & Assessment
Project	60.00%
End of Module Formal Examination	40.00%

Assessments

Full Time

No Continuous Assessment

Project

Assessment Type	Project	% of Total Mark	60
Timing	Week 12	Learning Outcomes	1,2,3
Non-marked	No
Assessment Description Practical programming project - the purpose of this applied project is to allow the learner, for example, to follow the data science process and prepare data so that statistical/ML techniques can be applied to the data to gain insights. This project may/may not have a significant group aspect at the discretion of the module lecturer and will typically involve a significant applied/programming component

No Practical

End of Module Formal Examination

Assessment Type	Formal Exam	% of Total Mark	40
Timing	End-of-Semester	Learning Outcomes	1,2,3
Non-marked	No
Assessment Description Final written en of module examination

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.
Reassessment Description Typically, learners are reassessed via a repeat of the End of Module Formal Examination

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		Contact	Lecture	12 Weeks per Stage	2.00	24
Estimated Learner Hours		Non Contact	Estimate Learner Hours	15 Weeks per Stage	5.13	77
Laboratory		Contact	Laboratory	12 Weeks per Stage	2.00	24
Total Weekly Contact Hours						4.00

Supplementary Book Resources
Module Resources
Grus J.. (2019), Data Science from Scratch: First Principles with Python, Second. All, O'Reilly, [ISBN: ISBN: 9781492]. Andreas C. Müller,Sarah Guido. (2019), Introduction to Machine Learning with Python, First edition - Fourth relaease. O'Reilly Media, p.376, [ISBN: 9781449369415]. Garrett Grolemund,Hadley Wickham. R for Data Science, First edition - Fourth release. [ISBN: 9781491910399].
This module does not have any article/paper resources
This module does not have any other resources

Discussion Note: