• 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

• 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

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.

• 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.