This course is designed to equip learners with the knowledge and practical skills required to collect, analyze, and interpret security-related data to detect, prevent, and respond to cyber threats. It introduces key concepts, tools, and techniques used in modern security operations, including data analytics, machine learning, and visualization, enabling learners to support decision-making in cybersecurity environments

By the end of this course, learners will be able to:

1. Analyze security data from multiple sources to identify potential threats and vulnerabilities.
   
2. Apply log analysis and SIEM tools to monitor, detect, and respond to security incidents.
   
3. Utilize machine learning techniques for threat detection and anomaly identification.
   
4. Interpret and visualize security data to support incident response and strategic decision-making

Introduction to Security Analytics

This topic introduces the concept of security analytics and its importance in detecting and responding to cyber threats. It covers the different types of analytics and explains the security analytics lifecycle, providing a foundation for understanding how data supports cybersecurity decisions.

Data Sources for Security Analytics

Learners explore various sources of security data, including logs, network traffic, and threat intelligence. The topic highlights data collection methods, as well as the importance of data quality and preprocessing for accurate analysis.

Security Operations Centre (SOC)

This section explains the role of a Security Operations Centre in monitoring and managing security incidents. It outlines SOC functions, key roles, and how analytics tools enhance threat detection and response.

Exploratory Security Data Analysis

This topic focuses on techniques for exploring and understanding security data. Learners identify patterns, trends, and anomalies using basic statistical and visualization methods.

Log Analysis Techniques

Learners examine how to analyze different types of logs to detect suspicious activities. The topic covers log parsing, correlation, and identifying indicators of compromise.

SIEM and Big Data for Security

This section introduces SIEM systems and their role in collecting and analyzing security data. It also highlights the use of big data technologies for handling large-scale security information.

Machine Learning for Threat Detection

Learners are introduced to machine learning techniques used to detect anomalies and predict threats. The topic covers basic concepts and challenges of applying ML in cybersecurity.

Security Data Visualisation

This topic explores how data visualization tools and techniques help present security insights clearly. It emphasizes the role of dashboards and charts in decision-making.

Incident Response Analytics

Learners understand how analytics supports the detection and response to security incidents. The topic covers the incident response process and the use of data in investigations.

Security Analytics Use Cases and Capstone

This final section presents real-world applications of security analytics and includes a capstone project where learners apply their knowledge to solve practical security problems.