Modules

Core Module Information
Module title: Data Analytics
SCQF level: 09:
SCQF credit value: 20.00
ECTS credit value: 10
Module code: SET09120
Module leader: Taoxin Peng
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

Requisites: AND Pre-requisite: A mathematics course covering algebra and statistics. A database systems course covering relational modelling AND AND Pre-requisite: [Module SET08120] Database Systems AND Pre-requisite: [Module SET07106] Mathematics for Software Engineering
Description of module content:

In this module you will gain a detailed insight into the practical and theoretical aspects of topics in data analysis, such as Data pre-processing, Data Analytics, Data Mining, and Data Visualisation. You will learn main techniques of analysing data, such as correlation, regression, forecasting, classification, clustering, including a variety of machine learning methods that are widely used in data analytics. While you are learning the fundamentals of data analytics, you will have opportunities to use a set of data analysing tools, such as Python, OpenRefine and Weka, to practice and enhance what you are learning.
Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Understand the concepts and process of data analysis.

LO2: Understand and critically evaluate modelling methods/techniques in Data Analysis.

LO3: Apply data processing techniques to process and prepare data for analysing

LO4: Apply data analysis algorithms to datasets to conduct data analysis.

LO5: Critically interpret and evaluate results generated by analysis techniques.
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: Taoxin Peng
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 The lectures cover both practical and theoretical aspects of topics in data analysis. You will learn main techniques of visualising and analysing data, such as correlation, regression, forecasting, classification, clustering, including a variety of machine learning methods that are widely used in data analytics.
Face To Face Practical classes and workshops 20 The practical will cover hands-on problems in data cleaning, transformation, visualization, statistical analysis, and developing regression, classification, and clustering models. It will also cover data analytics tools, including OpenRefine and Weka.
Online Guided independent study 160 Self-learning materials, including Book chapters, Notes/Tutorials/Blogs, videos, and Research articles, will be released on the Moodle page. The lecture slides and notebooks will also be released one week earlier before the lecture day.
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Practical Skills Assessment 40 1~2~3 Week 7 HOURS= 7 pages The assessment will be designed as practical coursework. The coursework aims to use OpenRefine to clean the given data, complete transformations, visualise the data and perform statistical analysis.
Practical Skills Assessment 60 1~2~3~4~5 Week 12 HOURS= 40 The assessment will be designed as practical coursework. The coursework aims to analyze data using different machine-learning algorithms and critically compare the performance of various models.
Component 1 subtotal: 100
Component 2 subtotal: 0
Module subtotal: 100

Indicative References and Reading List - URL:
SET09120 Data Analytics
 

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