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Session: 2022/23

Last modified: 10/01/2023 10:58:15

Title of Module: Machine Learning for Data Analytics

Code: COMP10082 SCQF Level: 10
(Scottish Credit and Qualifications Framework)
Credit Points: 20 ECTS: 10
(European Credit Transfer Scheme)
School:School of Computing, Engineering and Physical Sciences
Module Co-ordinator:Sean  Sturley

Summary of Module

This module introduces the student to the fundamental concepts of both Machine Learning and Data Analytics in order to provide students new methods and procedures to develop new insights into the vast array of data now available in todays business critical infrastructures. 

The module is complemented with labs where the concepts explained in lectures can be put in practices in order to get deeper understanding on the fundamentals of how machine learning can be used to analyse trends and anomalies within various data samples. 

Additionally, this module will work to develop a number of the key 'I am UWS' Graduate Attributes to make those who complete this module:


  • Critical Thinker
  • Ethically-minded
  • Research-minded

Work Ready

  • Problem-Solver
  • Effective Communicator
  • Ambitious


  • Autonomous
  • Resilient
  • Driven

The scope of the module includes the following topics:

  • Machine Learning Theory and Algorithms

  • Decision Trees

  • Supervised and Unsupervised Machine Learning

  • Reinforced Learning

  • Performance Analysis

  • Anomaly Detection

  • Data Mining and Analytics

Module Delivery Method
Face-To-FaceBlendedFully OnlineHybridCHybridOWork-based Learning
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Term used to describe the traditional classroom environment where the students and the lecturer meet synchronously in the same room for the whole provision.

A mode of delivery of a module or a programme that involves online and face-to-face delivery of learning, teaching and assessment activities, student support and feedback. A programme may be considered “blended” if it includes a combination of face-to-face, online and blended modules. If an online programme has any compulsory face-to-face and campus elements it must be described as blended with clearly articulated delivery information to manage student expectations

Fully Online
Instruction that is solely delivered by web-based or internet-based technologies. This term is used to describe the previously used terms distance learning and e learning.

Online with mandatory face-to-face learning on Campus

Online with optional face-to-face learning on Campus

Work-based Learning
Learning activities where the main location for the learning experience is in the workplace.

Campus(es) for Module Delivery
The module will normally be offered on the following campuses / or by Distance/Online Learning: (Provided viable student numbers permit)
Paisley:Ayr:Dumfries:Lanarkshire:London:Distance/Online Learning:Other:




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Term(s) for Module Delivery
(Provided viable student numbers permit).
Term 1check markTerm 2


Term 3


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Learning Outcomes: (maximum of 5 statements)

On successful completion of this module the student will be able to:

L1. Demonstrate a critical understanding of a range of machine learning approaches.

L2. Demonstrate detailed knowledge of the use of machne learning systems for data processing and analytics.

L3. Design and evaluate the performances of various machine learning methods for data analytics.

L4. Demonstrate the use of variuos problem solving techniques when preparing a variety of data sets for analysis

Employability Skills and Personal Development Planning (PDP) Skills
SCQF Headings During completion of this module, there will be an opportunity to achieve core skills in:
Knowledge and Understanding (K and U) SCQF Level 10.

The aim of the module is to enable the student to acquire the knowledge and understanding of Machine Learning through lectures, group practicals and guided self-study.

Practice: Applied Knowledge and Understanding SCQF Level 10.

Knowledge gained will be demonstrated through successful completion of coursework, laboratories and research.

Generic Cognitive skills SCQF Level 10.

Through the development of systems to analyse datasets as the student works through the lab work they will be able to apply these methodologies to other aspect of their work.

Communication, ICT and Numeracy Skills SCQF Level 10.

Throughout the lab program students will have to work together in the development and implementation of machine learning algorithms. Students will then have to write their own evaluation of the lab work so will have to use word processing, capturing and formatting of images and other computing skills.

Autonomy, Accountability and Working with others SCQF Level 10.

Various deadlines are imposed for the handing in of course work which requires the student to manage their time. The lab work has a small component of group working so the student will learn how to work within a group yet also fulfill their own personal work schedule.

Pre-requisites: Before undertaking this module the student should have undertaken the following:
Module Code:
Module Title:
Co-requisitesModule Code:
Module Title:

* Indicates that module descriptor is not published.

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Learning and Teaching
Learning Activities
During completion of this module, the learning activities undertaken to achieve the module learning outcomes are stated below:
Student Learning Hours
(Normally totalling 200 hours):
(Note: Learning hours include both contact hours and hours spent on other learning activities)
Lecture/Core Content Delivery12
Laboratory/Practical Demonstration/Workshop36
Independent Study52
Practice Based Learning100
200 Hours Total

**Indicative Resources: (eg. Core text, journals, internet access)

The following materials form essential underpinning for the module content and ultimately for the learning outcomes:

SHOW ME THE NUMBERS: Designing Tables and Graphs to Enlighten by Stephen Few. Analytics Press; 2nd ed. edition

Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic. John Wiley & Sons

Machine Learning For Absolute Beginners: A Plain English Introduction by Oliver Theobald.

Mastering Machine Learning Algorithms by Giuseppe Bonaccorso. Packt Publishing; 2nd edition

Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data by by Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen. Packt Publishing

(**N.B. Although reading lists should include current publications, students are advised (particularly for material marked with an asterisk*) to wait until the start of session for confirmation of the most up-to-date material)

Engagement Requirements

In line with the Academic Engagement Procedure, Students are defined as academically engaged if they are regularly engaged with timetabled teaching sessions, course-related learning resources including those in the Library and on the relevant learning platform, and complete assessments and submit these on time. Please refer to the Academic Engagement Procedure at the following link: Academic engagement procedure

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Supplemental Information

Programme BoardComputing
Assessment Results (Pass/Fail) No
Subject PanelApplied Computing
ModeratorGerry Creechan
External ExaminerM Davis
Accreditation Details
Version Number


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Assessment: (also refer to Assessment Outcomes Grids below)
Labs (20%) and Coursework One (30%)
Coursework Two (50%)
(N.B. (i) Assessment Outcomes Grids for the module (one for each component) can be found below which clearly demonstrate how the learning outcomes of the module will be assessed.
(ii) An indicative schedule listing approximate times within the academic calendar when assessment is likely to feature will be provided within the Student Handbook.)

Assessment Outcome Grids (Footnote A.)

Component 1
Assessment Type (Footnote B.) Learning Outcome (1) Learning Outcome (2) Learning Outcome (3) Learning Outcome (4) Weighting (%) of Assessment ElementTimetabled Contact Hours
Essaycheck markcheck mark  308
Portfolio of practical work  check markcheck mark2024

Component 2
Assessment Type (Footnote B.) Learning Outcome (1) Learning Outcome (2) Learning Outcome (3) Learning Outcome (4) Weighting (%) of Assessment ElementTimetabled Contact Hours
Report of practical/ field/ clinical workcheck markcheck markcheck markcheck mark5024
Combined Total For All Components100% 56 hours

A. Referred to within Assessment Section above
B. Identified in the Learning Outcome Section above

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  1. More than one assessment method can be used to assess individual learning outcomes.
  2. Schools are responsible for determining student contact hours. Please refer to University Policy on contact hours (extract contained within section 10 of the Module Descriptor guidance note).
    This will normally be variable across Schools, dependent on Programmes &/or Professional requirements.

Equality and Diversity

UWS Equality and Diversity Policy
(N.B. Every effort will be made by the University to accommodate any equality and diversity issues brought to the attention of the School)

2014 University of the West of Scotland

University of the West of Scotland is a Registered Scottish Charity.

Charity number SC002520.