Module Overview

Data Analytics

The aim of this module is to provide learners with the knowledge and understanding of a variety of data analytics techniques to discover actionable information in data. The module will give students an in depth understanding of exploratory data analysis and visualisation; data preparation; and data mining algorithms.

Module Code

COMP H4030

ECTS Credits


*Curricular information is subject to change

Introduction to Data Analytics

Data Mining and Knowledge Discovery. Data Mining Tasks and Applications. Methodologies: CRISP-DM

Data Understanding

ETL. Exploratory Data Analysis. Assess Data Quality. Data Visualisation. Reports and Dashboards.

Data Preparation

Data Cleaning: Handling Missing Data, Noisy Data and Outliers. Data Transformation: Smoothing, Normalisation. Data Reduction: Data aggregation, Dimensionality Reduction, Sampling.

Classification and Prediction

Training and Test Data. Classificartion Algorithms such as Decision Trees, Neural Networks, k-Nearest neighbour.Prediction algorimths: Regression.Model Evaluation.


Introduction to Clustering. Clustering algorimths such as K-means, DBScan, Agglomerative Clustering. Subjective and Objective cluster analysis. Distance measures. Introduction to Association Analysis.

Module Content & Assessment
Assessment Breakdown %
Other Assessment(s)40
Formal Examination60