Certification In Cybersecurity And Data Analytics
Published 9/2025
Created by Human and Emotion: CHRMI
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 63 Lectures ( 10h 8m ) | Size: 2.84 GB
Learn Fundamentals of Data Analytics for Cybersecurity, Tools, Network and Endpoint Security, Threat Detection, SOC
What you'll learn
You will understand the Introduction to Cybersecurity and Data Analytics, including core cybersecurity concepts
You'll also explore how data analytics supports threat detection and response. Hands-on activity includes analyzing a basic cybersecurity attack
You will explore the Fundamentals of Data Analytics for Cybersecurity, covering the four types of analytics-descriptive, diagnostic, predictive & prescriptive
You'll examine different cybersecurity data sources such as logs, network traffic, and endpoint data, and learn how to collect, store, and manage them
You will work with Tools for Cybersecurity Data Analytics, including SIEM platforms like Splunk, QRadar, and the Elastic Stack
You'll also gain exposure to TIPs and analytics tools such as Python (Pandas, Matplotlib, Seaborn), Tableau, and Power BI.
You will gain insights into Network and Endpoint Security Analytics, learning how to analyze network traffic for anomalies using tools like Wireshark
The role of machine learning in detecting suspicious activity on the network and endpoints will also be covered
You will gain expertise in Threat Detection and Incident Response, learning how to identify Indicators of Compromise (IoCs),
You'll explore automating incident response using data analytics. Practical work includes creating a threat dashboard and simulating an incident response
You will dive into Advanced Analytics for Cybersecurity, studying machine learning and AI applications such as predictive analytics
You will understand Security Operations Center (SOC) Analytics, focusing on SOC metrics, dashboards, and how analytics supports SOC functions
You'll explore workflow automation and review case studies of SOC success. Hands-on task involves designing a SOC dashboard using operational metrics
You will explore Compliance, Risk Management, and Privacy Analytics, learning how to ensure data privacy, manage regulatory requirements
You'll study insider threat detection techniques. Activities include generating a compliance report and analyzing user logs
You will examine Emerging Trends and Future Directions in cybersecurity, covering challenges in IoT, cloud, and AI environments
You'll explore data analytics in zero-trust architecture, big data analytics in threat hunting, and ethical considerations. Case-based activity focuses on cloud
Requirements
You should have an interest in cybersecurity, data analytics, and how modern analytics techniques are applied to detect and respond to cyber threats
A desire to learn how to collect, process, and analyze cybersecurity data using tools such as Python, Splunk, Tableau, Power BI, and SIEM platforms.
Interest in exploring real-world threat detection, incident response, and compliance use cases using analytical methods and cybersecurity frameworks
Familiarity with basic programming in Python and a foundational understanding of cybersecurity principles and data analysis techniques is recommended
Description
DescriptionTake 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
art 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. Introduction to Cybersecurity and Data Analytics1.1. Overview of Cybersecurity Concepts1.2. Importance of Data Analytics in Cybersecurity1.3. Role of Analytics in Threat Detection and Response1.4. Hands-On Activity - Explore a basic cybersecurity attack scenario and analyze it's components1.5. Conclusion of Introduction to Cybersecurity and Data AnalyticsModule 2. Fundamentals of Data Analytics for Cybersecurity2.1. Basics of Data Analytics - Descriptive, Diagnostic, Predictive and Prescriptive2.2. Data Sources for Cybersecurity Analytics2.3. Data Collection, Storage, and Management for Cybersecurity2.4. Hands-On Activity - Collect and Clean a Sample Dataset of System Logs2.5. Conclusion of Fundamentals of Data Analytics for CybersecurityModule 3. Tools for Cybersecurity Data Analytics3.1. SIEM (Security Information and Event Management) Platforms3.2. Threat Intelligence Platforms (TIPs)3.3. Data Analysis and Visualization Tools3.4. 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 AnalyticsModule 4. Network and Endpoint Security Analytics4.1. Analyzing Network Traffic for Anomalies4.2. Endpoint Security - Monitoring Devices for Threats4.3. Use of Machine Learning in Network and Endpoint Threat Detection4.4. Hands-On Activity - Perform Packet analysis on sample network traffic, Build a basic anomaly detection model using python4.5 Conclusion of Network and Endpoint Security AnalyticsModule 5. Threat Detection and Incident Response5.1. Identifying Indicators of Compromise (IoCs)5.2. Behavioral Analytics for Malware Detection5.3. Real - Time Threat Monitoring and Alerting5.4. Automating Incident Response with Analytics5.5. Hands-on Activity - Create a dashboard to monitor IoCs, Simulate an incident response workflow using sample data5.6. Conclusion of Threat Detection and Incident ResponseModule 6. Advanced Analytics for Cybersecurity6.1. Introduction to Machine Learning and AI in Cybersecurity6.2. Predictive Analytics for Threat Forecasting6.3. Natural Language Processing for Threat Intelligence6.4. Graph Analytics for Analyzing Attack Patterns6.5. Hands-On Activity - Build a Predictive model to forecast potential threats, Analyze relationships in a Cybersecurity dataset using graph analytics6.6. Conclusion of Advanced Analytics for CybersecurityModule 7. Security Operations Center ( SOC ) Analytics7.1. Role of Data Analytics in SOC Operations7.2. Key Metrics and Dashboards for SOC Teams7.3. Automating SOC Workflows with Analytics Tools7.4. Case Studies of SOC Success Stories7.5. Design a SOC Analytics dashboard using real-world metrics7.6. Conclusion of Security Operations Center (SOC) AnalyticsModule 8. Compliance, Risk Management, and Privacy Analytics8.1. Ensuring Data Privacy and Regulatory Compliance ( GDPR, HIPAA, etc. )8.2. Risk Assessment and Mitigation Strategies8.3. Monitoring User Behavior for Policy Violations8.4. Managing Insider Threats with Data Analytics8.5. Hands-On Activity - Develop a compliance report using a sample dataset, Analyze user activity logs for potential policy violations8.6. Conclusion of Compliance, Risk Management, and Privacy AnalyticsModule 9. Emerging Trends and Future Directions9.1. Cybersecurity Challenges in IoT, Cloud, and AI9.2. The Role of Data Analytics in Zero - Trust Architectures9.3. Advances in Threat Hunting with Big Data Analytics9.4. Ethical Considerations in Cybersecurity Analytics9.5. Hands-On Activity - Explore a case study on the use of analytics in cloud security9.6. Conclusion of Emerging Trends and Future DirectionsPart 2Capstone Project.Who this course is for
Aspiring cybersecurity analysts, data analysts, and SOC professionals who want to develop skills in using data analytics to detect and respond to cyber threats
IT professionals, network administrators, and system engineers looking to enhance their cybersecurity operations through data-driven monitoring and analysis
Data science and machine learning enthusiasts aiming to apply analytical techniques and automation to cybersecurity challenges.
Educators, researchers, and students interested in gaining practical experience with real-world cybersecurity analytics tools, workflows, and case studies.
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