Module Catalogue 2026/27

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

The aim of this module is to provide an overview of the big data problem and present the main principles and technologies behind distributed/parallel systems with data intensive applications.

Outline Of Syllabus

“Big Data” involves data whose volume, diversity and complexity requires new technologies, algorithms and analyses to extract valuable knowledge, which go beyond the normal processing capabilities of a single computer. The field of Big Data has many different faces such as databases, security and privacy, visualisation, computational infrastructure or data analytics/mining. This module will provide the following concepts:
1. Introduction to Big data: introducing the main principles behind distributed/parallel systems with data intensive applications, identifying key challenges: capture, store, search, analyse and visualise the data.
2. SQL Databases vs. NoSQL Databases: understand the growing amounts of data; the relational database management systems (RDBMS); overview of Structured Query Languages (e.g. SQL); introduction to NoSQL databases; understanding the difference between a relational DBMS and a NoSQL database; Identifying the need to employ a NoSQL DB.
3. Big Data frameworks and how to deal with big data: this includes the MapReduce programming model, as well as an overview of recent technologies (Hadoop ecosystem, and Apache Spark). Then, you will learn how to interact with the latest APIs of Apache Spark (RDDs, DataFrames and Datasets) to create distributed programs capable of dealing with big datasets (using Python and/or Scala)
4. Finally, we will dive into the data mining and machine learning part of the course, including data preprocessing approaches (to obtain quality data), distributed machine learning algorithms and data stream algorithms. To do so, you will use the Machine learning library of Apache Spark (MLlib) to understand how some machine learning algorithms (e.g. Decision Trees, Random Forests, k-means) can be deployed at a scale.

Intended Knowledge Outcomes

Knowledge and Understanding: Understand the importance of the data. The principles that allow the processing of big data sets. Understand the working and features of existing machine learning algorithms capable of handling big data. Learn to use the main tools of the big data ecosystem. The current limitations of big data technologies to allow distributed machine learning.

Intended Skill Outcomes

Intellectual Skills: Understand complex ideas and relate them to specific problems or questions in the area of parallel computation. Be able to identify distributed solutions/approaches to handle big datasets with existing technologies.
Professional/Practical Skills: Hands-on experience with state-of-the-art technologies to handle big data.
Transferable/Key Skills: Experience in problem solving. Experience in working in groups. Retrieve information from appropriate sources (e.g. Spark API)

Teaching Rationale And Relationship

Teaching methods are set by Nottingham University

Assessment Rationale And Relationship

Assessment is set by Nottingham University

General Notes

N/A