• Introduction to Artificial Intelligence
• Industrial applications of AI
• Bias and fairness in AI Systems

• Descriptive Statistics and Exploratory Data Analytics
• Statistical Inferences
• Linear Regression
• Logistic Regression Analysis
• Clustering and Introduction to Unsupervised Learning

• Sentiment Analysis
• Topic Modelling

Predict Attrition & Optimize Hiring

• Designing Interactive Dashboards on PowerBI
• HR metrics and Dashboards
• Workforce Predictive Modelling
• Turnover Predictive Modelling
• Managing Employee Performance using Analytics

Customer Segmentation & ROI

• Introduction to marketing analytics
• Segmentation and Positioning
• Product analytics
• Marketing mix modelling
• Consumer analytics

Supply Chain Forecasting

• Fundamentals of Operation Analytics
• Retail and e-commerce operations
• Product Process Matrix
• Theory of Constraints
• Quality Management (Lean & Six-Sigma)
• Forecasting

• The Data Landscape: Understanding different types of data and the data lifecycle
• Excel Essentials: Cleaning, structuring, and analyzing data using basic spreadsheet
• Python for Analytics: Mastering core Python syntax, variables, and data structures
• SQL Fundamentals: Basic querying and managing data in a relational database
• The AI Ecosystem: A high-level, non-technical overview of AI, Machine Learning, and how Generative AI fits into this picture

• What is an LLM?: Understanding the basic architecture and capabilities of models like GPT and Gemini
• Prompt Engineering Techniques: Mastering core prompting strategies, from zero-shot to few-shot prompting
• AI-Assisted Writing & Analysis: Using LLMs to summarize articles, generate reports, and assist with creative tasks
• Understanding Tokens: A conceptual overview of how models process information.
• Exploring Open-Source LLMs: An introduction to publicly available models and their use cases
• Interacting with the API: A simple introduction to calling the OpenAI API with Python

• Data Cleaning with AI: Leveraging AI tools to automatically find and fix errors in datasets
• Exploratory Data Analysis (EDA): Using Numpy and Pandas to generate initial hypotheses and summarize key statistics from your data
• AI-Powered Visualization: Creating stunning and informative charts and graphs with the help of AI

• The Anatomy of an AI Agent: Understanding the core components of autonomous agents, including planning, memory, and tool use
• Building with Frameworks: Getting hands-on with popular frameworks like LangChain and CrewAI to design and build multi-agent systems
• AI Automation & Tool Use: Creating agents that can interact with APIs and online resources to perform tasks like research

• In this module, you will design and build a Capstone Project from scratch, showcasing your ability to apply Generative and Agentic AI principles to a real-world problem.

• Introduction to product management and its lifecycle
• Understanding customer needs and market research techniques
• Defining product vision, mission, and strategy
• Key metrics and KPIs in product management

• Advanced product development and roadmap planning
• Managing cross-functional teams and stakeholder communication
• Scaling products and optimizing customer experiences
• Navigating complex product challenges and decision-making

• Strategic analysis and competitive advantage
• Market segmentation and target audience identification
• Business models, revenue streams, and cost structures
• Growth hacking techniques and sustainable scaling strategies

• Understanding AI technologies and their impact on products
• Integrating AI solutions into product management
• Building AI-driven product features and functionalities
• Ethics and challenges of implementing AI in products

• Introduction to agentic AI and its potential applications
• Ethical considerations in AI development and deployment
• Addressing bias, fairness, and accountability in AI
• Building responsible AI systems with a human-centered approach

• Applying learning to design an AI-driven product
• Conducting market validation and feasibility studies
• Crafting a compelling pitch for stakeholders and investors
• Final presentation and product showcase

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

• Welcome to the Future of Digital Marketing
• Marketing Mix 4.0
• 7Ps of Marketing
• Consumer Behaviour and Branding in the Digital Economy
• Integrated Marketing Communications - Journey Mapping (AIDAS)
• Guest Speaker 1

• Introduction to Digital Marketing and its elements
• Website, Mobile Apps and UX/UI
• Search Engine Optimisation in the AI world
• Search Engine Marketing
• Social Media Marketing and role of AI
• Influencer & Community Marketing, AI Influncers
• Display Advertising
• Email Campaigns
• Online Reputation Management
• Viral Loops & Growth Hacking

• Introduction to MarTec
• GenAI 101 for Marketers
• AI Image, Audio, and Video Creation
• Privacy, Ethics & Trust in AI Marketing
• Emerging Trends: Agentic AI, Personal AI & Hyperautomation

• Display Ads and Targeting.
• Social Media Strategy.
• Influencers, Communities, and Online Reputation.

• Introduction to Digital Marketing Analytics
• MR Using AI Tools & Customer Segmentation
• Customer Journey and Attribution Models
• A/B Testing and Optimisation Strategies
• Social Media & Influencer Analytics; Predictive Analytics
• Dashboards & Visualisation for Marketing Teams
• Data Driven Customer Retention

• Building a Personal Brand with LinkedIn and AI Tools
• Digital Marketer Roles: Mock Interviews & Roleplay
• Summary and Concluding Session, Set up for Projects

• 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.