Certification in Cybersecurity and Data Analytics

Description

Take the next step in your cybersecurity and analytics journey! Whether you're an aspiring cybersecurity analyst, data scientist, IT professional, or business leader, this course will equip you with the skills to harness data analytics for scalable, real-world cybersecurity solutions. Learn how tools like SIEM platforms, Python, Tableau, and machine learning are transforming threat detection, incident response, and risk management through data-driven intelligence and automation.

Guided by hands-on projects and real-world use cases, you will:

• Master foundational cybersecurity concepts and analytics workflows applied to real-time security scenarios.

• Gain hands-on experience collecting, managing, and analyzing data from sources like logs, network traffic, and endpoints.

• Learn to detect anomalies, visualize threats, and build predictive models for proactive cybersecurity defense.

• Explore industry applications in SOC operations, compliance management, insider threat detection, and threat intelligence.

• Understand best practices for security automation, privacy, and ethical data use in analytics-driven security operations.

• Position yourself for a competitive advantage by developing in-demand skills at the intersection of cybersecurity, data analytics, and machine learning.

The Frameworks of the Course

• Engaging video lectures, case studies, projects, downloadable resources, and interactive exercises—designed to help you deeply understand how to apply data analytics in cybersecurity operations and threat detection.

• The course includes industry-specific case studies, security tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to analyze threats, respond to incidents, and manage cybersecurity risks using data-driven approaches.

• In the first part of the course, you’ll learn the basics of cybersecurity, data analytics, and how analytical methods enhance security posture and threat intelligence.

• In the middle part of the course, you will gain hands-on experience using tools like SIEM platforms, Python, Power BI, and Splunk to collect, analyze, and visualize security-related data across different stages of the cybersecurity lifecycle.

• In the final part of the course, you will explore automation strategies, compliance analytics, emerging trends, and real-world applications across industries. All your queries will be addressed within 48 hours with full support throughout your learning journey.

Course Content:

Part 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. Introduction to Cybersecurity and Data Analytics

1.1. Overview of Cybersecurity Concepts

1.2. Importance of Data Analytics in Cybersecurity

1.3. Role of Analytics in Threat Detection and Response

1.4. Hands-On Activity - Explore a basic cybersecurity attack scenario and analyze it's components

1.5. Conclusion of Introduction to Cybersecurity and Data Analytics

Module 2. Fundamentals of Data Analytics for Cybersecurity

2.1. Basics of Data Analytics - Descriptive, Diagnostic, Predictive and Prescriptive

2.2. Data Sources for Cybersecurity Analytics

2.3. Data Collection, Storage, and Management for Cybersecurity

2.4. Hands-On Activity - Collect and Clean a Sample Dataset of System Logs

2.5. Conclusion of Fundamentals of Data Analytics for Cybersecurity

Module 3. Tools for Cybersecurity Data Analytics

3.1. SIEM (Security Information and Event Management) Platforms

3.2. Threat Intelligence Platforms (TIPs)

3.3. Data Analysis and Visualization Tools

3.4. Hands-On Activity - Analyze log data using Splunk or a similar platform, Visualize attack patterns using Tableau or Python.

3.5. Conclusion of Tools for Cybersecurity Data Analytics

Module 4. Network and Endpoint Security Analytics

4.1. Analyzing Network Traffic for Anomalies

4.2. Endpoint Security - Monitoring Devices for Threats

4.3. Use of Machine Learning in Network and Endpoint Threat Detection

4.4. Hands-On Activity - Perform Packet analysis on sample network traffic, Build a basic anomaly detection model using python

4.5 Conclusion of Network and Endpoint Security Analytics

Module 5. Threat Detection and Incident Response

5.1. Identifying Indicators of Compromise (IoCs)

5.2. Behavioral Analytics for Malware Detection

5.3. Real - Time Threat Monitoring and Alerting

5.4. Automating Incident Response with Analytics

5.5. Hands-on Activity - Create a dashboard to monitor IoCs, Simulate an incident response workflow using sample data

5.6. Conclusion of Threat Detection and Incident Response

Module 6. Advanced Analytics for Cybersecurity

6.1. Introduction to Machine Learning and AI in Cybersecurity

6.2. Predictive Analytics for Threat Forecasting

6.3. Natural Language Processing for Threat Intelligence

6.4. Graph Analytics for Analyzing Attack Patterns

6.5. Hands-On Activity - Build a Predictive model to forecast potential threats, Analyze relationships in a Cybersecurity dataset using graph analytics

6.6. Conclusion of Advanced Analytics for Cybersecurity

Module 7. Security Operations Center ( SOC ) Analytics

7.1. Role of Data Analytics in SOC Operations

7.2. Key Metrics and Dashboards for SOC Teams

7.3. Automating SOC Workflows with Analytics Tools

7.4. Case Studies of SOC Success Stories

7.5. Design a SOC Analytics dashboard using real-world metrics

7.6. Conclusion of Security Operations Center (SOC) Analytics

Module 8. Compliance, Risk Management, and Privacy Analytics

8.1. Ensuring Data Privacy and Regulatory Compliance ( GDPR, HIPAA, etc. )

8.2. Risk Assessment and Mitigation Strategies

8.3. Monitoring User Behavior for Policy Violations

8.4. Managing Insider Threats with Data Analytics

8.5. Hands-On Activity - Develop a compliance report using a sample dataset, Analyze user activity logs for potential policy violations

8.6. Conclusion of Compliance, Risk Management, and Privacy Analytics

Module 9. Emerging Trends and Future Directions

9.1. Cybersecurity Challenges in IoT, Cloud, and AI

9.2. The Role of Data Analytics in Zero - Trust Architectures

9.3. Advances in Threat Hunting with Big Data Analytics

9.4. Ethical Considerations in Cybersecurity Analytics

9.5. Hands-On Activity - Explore a case study on the use of analytics in cloud security

9.6. Conclusion of Emerging Trends and Future Directions

Part 2

Capstone Project.