1.1 Introduction to Cybersecurity Fundamentals
• Core concepts: Confidentiality, Integrity, Availability (CIA Triad)
• Threat actors & attack vectors
• Key principles: Defense-in-Depth, Least Privilege, Zero Trust
• Frameworks & standards: NIST, ISO 27001 (overview), CIS Controls
1.2 Networking Essentials for Security
• TCP/IP model & key protocols
• Network devices and their security roles
• Topologies, segmentation & wireless security
• Tools: Wireshark, tcpdump
• Lab: Network-traffic analysis with Wireshark
1.3 Understanding the Evolving Threat Landscape
• Current & emerging cyber-threat trends
• Impact of new technologies on security
• Threat-intelligence fundamentals
• Lab: Case-study analysis of a recent cyber-attack

2.1 Ethical Hacking Methodologies
• Phases of ethical hacking
• Legal & ethical considerations
• Intro to penetration-testing toolsets
• Tools: Kali Linux
• Lab: Setting up Kali VM & vulnerable targets
2.2 Footprinting & Reconnaissance Techniques
• Passive reconnaissance (OSINT)
• Active reconnaissance: scanning & enumeration
• Tools: Nmap, Shodan, theHarvester, dig / nslookup
• Lab: Comprehensive OSINT + active recon
2.3 Vulnerability Assessment Strategies
• Vulnerability-management lifecycle
• Scanning tools & workflows
• CVE / CVSS basics; spotting common vulns
• Tools: OpenVAS, Nessus (trial)
• Lab: Run a vulnerability scan & interpret results

3.1 Advanced Network-Security Controls
• Firewalls deep dive: WAFs, NGFWs
• IDS / IPS: signature vs. anomaly detection
• VPNs, NAC, DDoS mitigation
• Tools: Snort, Wazuh, iptables
• Lab: Configure firewall rules & analyse IDS/IPS alerts
3.2 Security Operations Centre (SOC) & SIEM
• SOC roles, tiers & workflows
• SIEM basics: log collection, correlation, alerting
• Tools: Splunk (community), ELK Stack
• Lab: Security-log analysis inside a SIEM
3.3 Comprehensive Incident Response
• Incident-response lifecycle & planning
• Digital-forensics basics for IR
• Effective incident communication
• Lab: Build a basic IR playbook

4.1 Secure-SDLC Integration & Threat Modelling
• Embedding security in each SDLC phase
• STRIDE threat-modelling workshop
• Security-by-design principles
• Lab: Build a simple threat model
4.2 Web & API Vulnerabilities (OWASP)
• OWASP Top 10 deep dive
• API-security best practices
• Tools: Burp Suite (community), OWASP ZAP, SQLMap
• Lab: Exploit common web/API vulns in a safe environment
4.3 Secure Coding Best Practices
• Data protection & cryptography essentials
• Secure configuration, error handling & logging
• Dependency / supply-chain security
• Tools: Bandit, Snyk (SCA)
• Lab: Code review & remediation exercise

5.1 Leveraging AI/ML for Cybersecurity
• Fundamentals of ML in security contexts
• Use-cases: anomaly detection, malware classification, etc.
• Data requirements & pitfalls
• Tools: scikit-learn, TensorFlow (basic)
• Lab: Train a simple malware-detection model
5.2 Security Automation & Orchestration with AI
• SOAR platform concepts
• Automated incident triage & response
• AI-driven vulnerability prioritisation
• Tools: Cortex (concepts), TheHive, Python scripting
• Lab: Design an automated security workflow
5.3 Adversarial AI & Defensive Techniques
• How attackers exploit AI/ML
• Basics of adversarial machine-learning (AML)
• Defensive counter-measures
• Tools: IBM ART, Google AutoML (conceptual)
• Lab: Identify & discuss adversarial examples

Work on projects like
• Cybersecurity Foundations (Advanced Refresh)
• Compliance Gap Analysis Report

Business problem framing, KPIs, Excel modelling, basic statistics, MySQL, distributions, hypothesis testing, A/B testing.

Visualization Best Practices & Perception, Choosing the Right Chart, Introduction to Tableau and Power BI, Building Interactive Dashboards, Data Storytelling Frameworks, Advanced Visualization Techniques.

Data is the foundation for Artificial Intelligence models. Generative AI models rely on large amounts of high-quality data to learn patterns, relationships, and structures. Understanding various data stores helps students appreciate how data is stored, managed, and retrieved. This data is usually stored in a variety of data stores viz. relational databases, NoSQL databases, data lakes. Knowledge of these data stores will enable students process various data formats

Learn basic Python required for GenAI- Data Types, Flow Structures, Data Structures, Functions, Libraries, Python in Data Science.

Supervised and unsupervised learning, time series analysis, churn prediction, forecasting. Examples include sentiment analysis for customer reviews.

Understand the transition from traditional machine learning to neural networks and deep learning and their common applications.

You can use LLM for content creation, computer programming, problem solving, dataset generation, and learning. In this model, you will learning how to effectively leverage LLM to improve your productivity. You will be learning basic prompting strategies such as setting the context, role prompting, and tuning model settings. You will also learn advanced prompting methods such as chain-of-thought (COT) prompting.

Supervised and unsupervised learning, time series analysis, churn prediction, forecasting.

• Descriptive vs Predictive vs Prescriptive
• Causal Inference
• Designing Business Experiments (A/B Testing)
• Hypothesis Testing logic
• Moving from 'Data says X' to 'We should do Y'.

• The Anatomy of a Decision
• Decision Biases (Anchoring, Confirmation)
• Decision Matrix Analysis
• The 'Pre-Mortem' Technique
• Using AI to stress-test decisions
• Metrics for Decision Quality.

• Buy vs Build in AI
• Understanding RAG (Retrieval Augmented Generation) conceptually
• Working with Data/Tech teams
• Defining AI Requirements
• Automation possibilities vs limitations
• Data Privacy & Governance.

• Learn core linear algebra concepts like vectors, matrices, echelon form, and solving systems of linear equations.
• Understand vector spaces, basis, linear transformations, and projections crucial for AI and ML models.
• Build calculus foundations including functions, gradients, partial derivatives, and the Jacobian and Hessian matrices.
• Grasp key probability concepts such as conditional probability, Bayes’ theorem, random variables, and joint distributions.

• Introduce core supervised learning concepts, including regression and classification.
• Cover essential unsupervised learning methods, particularly clustering and dimensionality reduction.
• Introduce ensemble methods and model selection strategies.
• Provide a strong basis in evaluation metrics and best practices for validating machine learning models.

• Master the fundamentals of deep neural networks and advanced architectures (CNNs, RNNs, transformers).
• Understand and implement generative models (VAEs, GANs) for creative AI applications.
• Explore specialized AI topics: NLP, computer vision, and reinforcement learning.
• Develop insight into research frontiers and future directions in AI.