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.
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.
Clustering
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 Examination | 60 |