Modules

Core Module Information
Module title: Data Analytics
SCQF level: 11:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET11122
Module leader: Kehinde Babaagba
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

There are no pre-requisites for this module to be added
Description of module content:

The aim of this module is to enable you to develop a deep understanding of the fundamentals of data analytics, and to give you opportunities to practise a set of popular data analytical tools. Topics covered include:*Data Pre-processing – data quality, data cleaning, data preparation*Data Analytics – techniques of analysing data, such as classification, association, clustering and visualisation, including a variety of machine learning methods that are widely used in data mining* Post processing – data visualisation, interpretation, evaluationThis module will use tools such as OpenRefine, Weka and Tableau for standard and structured data The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools and practical skills in deployment and use of tools and critical evaluation in addition to providing useful generic skills for employment.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Critically analyse the concepts and process of data analytics.

LO2: Critically evaluate methods/techniques in data analytics.

LO3: Examine and apply data analytics algorithms to datasets to conduct data analysis and visualisation, by testing data analytical tools.

LO4: Critically interpret and evaluate results generated by analytical techniques.

LO5: Investigate and critically reflect on current research topics in data analytics.
Full Details of Teaching and Assessment
2025/6, Trimester 1, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Kehinde Babaagba
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 Lectures cover data cleaning, processing and visualization. They are also an opportunity for group discussion on best methods and for understanding the software.
Face To Face Practical classes and workshops 20 A lab session that focuses on the practical implementation of the data analysis framework, prototyping and pipeline testing.
Online Guided independent study 160 Guided independent study, students are expected to explore the topics towards writing a report outlining best-fit techniques for their pipeline.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Report 20 1~5 Week 5 , WORDS= 1,500 & dataset The first part consists of a 5 page report on the errors fixed during the data cleaning process and a short reflection to give insights on how the process can improved + the cleaned datasets that will be tested by the ML to make sure they are formatted and can be parsed my the ML models.
Project - Practical 80 1~2~3~4~5 Week 10 HOURS= 2,500 A 2,500 word reflective report with visualisations and interpretation of results for best fit modules to the questions selected, a zipped file with all the datasets.
Component 1 subtotal: 20
Component 2 subtotal: 80
Module subtotal: 100

Indicative References and Reading List - URL:
SET11122 / SET11822 Data Analytics
 

Related information