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Executive IIT 🧑‍💻 For Working Professionals ✦︎ AI-First

Executive Program in AI for E-Commerce and Quick Commerce

Indian Institute Of Technology Roorkee (IIT Roorkee)
Indian Institute Of Technology Roorkee (IIT Roorkee)
Duration

6 Months

Type

Executive

Mode

Online

₹90,000

Total Program Fees

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Program Overview

The Executive Program in AI for E-Commerce & Quick Commerce is designed for professionals seeking to apply artificial intelligence across digital commerce operations. The program combines machine learning, NLP, Generative AI and big data with applications in recommendations, forecasting, pricing, fraud detection and customer analytics. Through live online sessions, case-based learning and a capstone project, participants learn to convert commerce data into actionable business decisions.

Program Highlights

Build applied expertise in AI, machine learning, NLP, Generative AI and big data for digital commerce.
Explore practical applications across recommendations, demand forecasting, pricing, fulfilment and fraud detection.
Learn through real-world case studies from leading e-commerce and quick-commerce businesses.
Gain hands-on exposure to tools such as Python, Power BI, Tableau, Elasticsearch and LLM-based systems.
Participate in optional campus immersion opportunities and earn a certificate from CEC, IIT Roorkee.

Curriculum

Module I: Foundations of AI & Data
  • AI Evolution: Traditional E-Commerce vs. AI-First & Q-Commerce
  • Data Types I: Structured Data (Transactions Logistics Inventory)
  • Data Types II: Unstructured Data (Clickstream Images Reviews)
  • The Data Pipeline I: Data Flow from Clickstream to Order Management
  • The Data Pipeline II: Integration with Logistics and Fulfilment Systems
  • Case Study: Meesho – A Game-Changer in Indian E-Commerce
  • Data Architecture: Introduction to Data Lakes and Warehouses
  • Real-Time Pipelines: Streaming Data for Immediate Insights
  • Data Capture: Implicit Signals (Dwell Time Heatmaps) vs. Explicit Ratings
  • Identity Resolution: Stitching User Journeys Across Web App and Offline Channels
  • Ecosystem Strategy: Platform-Based Growth and Diversification
  • Case Study: Alibaba Group – Fostering an E-Commerce Ecosystem
  • Python for Retail: Key Libraries (Pandas PySpark) for Commerce
  • Q-Commerce Economics: Unit Economics Speed and Profitability Models
  • Fulfilment Models: 10-Minute Delivery vs. Next-Day Delivery Logistics
  • Governance: Handling PII GDPR and DPDP Compliance in Retail
  • Case Study: Kent County Council – Implementing IT for E-Government
  • Case Study: The Ultimate Bluff – Partygaming.com
Module II: EDA & Visualization
  • Data Hygiene: Automated Cleaning of Catalogue Data (Missing Attributes)
  • Outlier Detection I: Identifying Bulk Buyers and Resellers
  • Outlier Detection II: Detecting Pricing Glitches and Anomalies
  • Feature Engineering I: Creating Temporal Features (Days Since Last Purchase)
  • Feature Engineering II: Behavioural Features (Average Basket Size Return Rate)
  • Case Study: Data Modelling and Management for Big Data
  • Segmentation Logic: Introduction to K-Means Clustering
  • User Grouping: Implementing Segmentation on Transaction Data
  • RFM Analysis I: Calculating Recency Frequency and Monetary Scores
  • RFM Analysis II: Interpreting Scores for Marketing Action
  • Cohort Analysis: Tracking Customer Retention Month-over-Month
  • Case Study: DesiFirangi.com – Building a Niche E-Commerce Portal
  • Geospatial Analytics I: Mapping Demand Density
  • Geospatial Analytics II: Heatmaps for Dark Store Planning
  • Dashboarding 101: Building a Command Centre in Power BI or Tableau
  • Metric Tracking: Real-Time Monitoring of Order Volumes and Cancellations
  • Sales Estimation: Applying Analytics to Predict Sales
  • Case Study: E-Commerce Analytics for CPG Firms (A)
  • Funnel Visualisation: Mapping View to Add-to-Cart to Checkout
  • Drop-Off Analysis: Identifying Friction Points in the User Journey
  • Cart Abandonment: Analytics for Recovery Strategies
  • Advanced Storytelling: Visualising Conversion Rates Effectively
  • Visualising Cohorts: Creating Heatmaps for Long-Term Retention
  • Visualisation Project: Building the Executive Dashboard Prototype
Campus Immersion 1
  • Two-Day Campus Immersion at IIT Roorkee
  • Faculty Sessions
  • Industry Networking
  • Executive Reporting: Automating Weekly Business Review Slides
  • Workshop on Storytelling with Data
  • Peer Group Assignments
  • Question-and-Answer Session with Industry Mentors
Module 3: Rec Systems
  • Recommendation-System Fundamentals: Understanding the User-Item Matrix
  • Sparsity Issues: Handling Missing Data in Large Catalogues
  • Collaborative Filtering I: User-Based vs. Item-Based Approaches
  • Collaborative Filtering II: Matrix Factorisation Techniques
  • Content-Based Filtering: Using Metadata and Descriptions
  • The Cold Start Problem: Handling New Users or Items with No History
  • Hybrid Systems: Combining Behavioural Data with Product Metadata
  • Context Awareness: Injecting Time Weather and Location Signals
  • Session-Based Recommendations I: Handling Anonymous Users
  • Session-Based Recommendations II: Real-Time Product Suggestions
  • Architecture: Latency Requirements for Live Recommendations
  • Real-World Examples: Analysing Recommendation-System Architectures in Action
  • Upsell Strategy: Algorithms for Recommending Premium Versions
  • Cross-Sell Strategy: Frequently Bought Together Affinity Mapping
  • Evaluation Metrics I: Precision at K and Recall
  • Evaluation Metrics II: Normalised Discounted Cumulative Gain
  • Affinity Mapping: Association Rule Mining Logic
  • Case Study: Housing.com – Disrupting the House Search Process
Module 4: Forecasting & Ops
  • Time-Series Fundamentals: Decomposing Trend Seasonality and Noise
  • Statistical Models: Implementation of ARIMA
  • Prophet Model: Facebook Prophet for Retail Forecasting
  • Event Modelling: Handling Shock Events (Flash Sales Rain)
  • Baseline Metrics: Calculating MAPE and RMSE
  • Case Study: The Internet of Things – Shaping the Future of E-Commerce
  • Deep Learning: LSTMs for Non-Linear Demand Patterns
  • Hyperlocal Demand: Predicting Demand by Pincode or Zone
  • Spike Detection: Managing High-Velocity Order Influxes
  • Platform Strategy: Curated vs. Open Marketplace Models
  • Supply-Chain Statistics: Interpreting Optimisation Outputs
  • Case Study: Pepperfry.com – Turning the Tables on Disruption
  • Inventory Balancing: Inter-Store Stock Transfer Algorithms
  • Perishability AI: Predicting Wastage for Fresh Grocery
  • Replenishment Logic: Automated Reorder-Point Calculation
  • Bullwhip Effect: Supply-Chain Dynamics in Perishables
  • Case Study: Warehousing Enhancements for E-Commerce Growth
  • Case Study: Easy Flower – Flowers Meet Business and Technology
  • Route Optimisation: Solving Vehicle-Routing Problems
  • ETA Prediction: Machine Learning for Accurate Delivery-Time Estimation
  • Batching Logic: Combining Multiple Orders for Individual Riders
  • Rider Allocation: Matching Supply to Demand Spikes
  • Case Study: Improving Last-Mile Productivity at Paack
  • Case Study: Snapdeal – A Nightmare or a Benefit in Reverse Logistics?
  • Price Elasticity: Calculating Sensitivity to Price Changes
  • Dynamic Pricing I: Rules-Based vs. AI-Based Strategies
  • Dynamic Pricing II: Reinforcement Learning for Surge Pricing
  • Markdown Optimisation: Optimal Discounting for Clearance
  • Surge Logic: Algorithms Behind Peak-Hour Pricing
  • Case Study: Lessons from More Than 1000 E-Commerce Pricing Tests
Module 5: Customer Analytics
  • Customer Lifetime Value Basics
  • Marketing-Mix Modelling: Budget Allocation Across Facebook and Google
  • Attribution Modelling I: Last-Click vs. First-Click
  • Attribution Modelling II: Algorithmic Attribution
  • ROAS Optimisation: AI in Digital Advertising Optimisation
  • Growth Strategy: Data-Led Growth in Competitive Markets
  • Transaction Fraud: Detecting Stolen Cards and Payment Anomalies
  • Abuse Detection: Identifying Wardrobing or Return Abuse
  • Bot Detection: Stopping Promotional-Code Hunting Scripts
  • Risk Pipelines: Real-Time Fraud-Detection Architectures
  • Ethical Regulation: Regulatory Challenges in Internet Models
  • Industry Example: PayPal and Stripe Real-Time Fraud Pipelines
Module 6: GenAI & NLP
  • NLP Foundations: Tokenisation and Sentiment Analysis
  • Entity Extraction: Extracting Brand and Size from Search
  • Vector Databases: Introduction to Embeddings
  • Semantic Search: Moving Beyond Keyword Matching
  • Review Mining: Extracting Product Defects from User Feedback
  • Case Study: Twiggle – E-Commerce with Semantic Search
  • LLM Integration: Fine-Tuning Large Language Models for Commerce
  • RAG Architecture I: Retrieval-Augmented Generation Basics
  • RAG Architecture II: Chat with Catalogue Implementation
  • Support Automation: Flows for Where Is My Order Tickets
  • AI Agents: Deploying Autonomous Support Agents
  • Industry Case: Klarna and Intercom AI Support Agents
  • Content Factory: Generating Product Titles and Descriptions
  • Visual AI: Automatically Tagging Product Images
  • Multilingual AI: Real-Time Translation for Tier-2 and Tier-3 Markets
  • Voice Commerce: Speech-to-Text for Mobile-First Users
  • Generative Design: AI for Marketing Creatives
  • Generative AI Ethics: Bias and Hallucination in Commerce
Module 7: Capstone Project
  • Problem Selection: Choosing a Track (Recommendations Churn Pricing)
  • Data Preparation I: Acquiring the Required Dataset
  • Data Preparation II: Cleaning and Feature Engineering
  • Metric Definition: Business KPIs vs. Model KPIs
  • Execution Risk: Trade-Offs Between Speed and Stability
  • Case Study: 24x7 at Full Speed – Accelerated Time to Market
  • Baseline Modelling: Establishing a Dummy-Model Benchmark
  • Advanced Modelling: Hyperparameter Tuning and Iteration
  • Deployment I: Creating a Streamlit Dashboard or API
  • Deployment II: Model Serving and Latency Optimisation
  • Insight Generation: Translating Model Outputs into Business Slides
  • Presentation Preparation: Structuring the Defence Story
Campus Immersion 2: Project Defense & Career Guidance
  • Final Capstone Defence: Project Presentations
  • Peer Review and Feedback
  • Career Guidance: Resume Reviews and AI Roles
  • Strategic Scaling: Innovation in Emerging Markets
  • Case Study: Building India’s Leading E-Commerce Company – mjunction
  • Certification and Programme Wrap-Up

Learning Outcomes

1
Apply AI and machine learning techniques to solve high-impact e-commerce and quick-commerce challenges.
2
Design recommendation systems, demand-forecasting models and intelligent search solutions.
3
Optimise pricing, inventory, fulfilment and last-mile operations using data-driven approaches.
4
Analyse transaction, clickstream and customer-behaviour data to generate actionable insights.
5
Translate AI outputs into clear business recommendations for product, marketing and operations teams.

Eligibility Criteria

Target Audience: Professionals currently working in or aspiring to enter the retail, e-commerce, or quick commerce sectors who wish to integrate AI into their operations.
Technical Background: Individuals with a foundational understanding of data analytics (e.g., proficiency in Excel) who are ready to transition to Python-based data science workflows.
Educational Level: Graduates or diploma holders seeking an executive-level understanding of how AI architectures drive modern digital commerce businesses.

Fees & Financing

Program Fee

Year 1

₹90,000

Yearly Sum

Year 1

₹90,000

Total Program Fees

1 year · all components included

₹90,000

💰 EMI Options Available

Starting from ₹7,500/month with 0% interest for first 6 months. Partner banks: HDFC, ICICI, Axis Bank.

About Indian Institute Of Technology Roorkee (IIT Roorkee)

Indian Institute Of Technology Roorkee (IIT Roorkee)

Indian Institute Of Technology Roorkee (IIT Roorkee)

The Executive Program in AI for E-Commerce & Quick Commerce by the Continuing Education Centre (CEC), IIT Roorkee equips professionals to understand how AI powers modern digital retail. With e-commerce platforms generating vast data, organisations rely on AI to drive efficiency, speed, and customer experience. This program covers key applications of machine learning, NLP, generative AI, and big data across search, recommendations, demand forecasting, pricing, and fraud detection. Through hands-on learning and real-world problem solving, participants learn to turn insights into action. Delivered via live online sessions and campus immersion, it enables professionals to bridge analytics with business execution.

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Frequently Asked Questions

What is the IIT Roorkee Executive Program in AI for E-Commerce & Quick Commerce?
The Executive Program in AI for E-Commerce & Quick Commerce, offered through the Continuing Education Centre (CEC), IIT Roorkee, is a 6-month programme designed to help professionals understand how artificial intelligence enhances digital retail operations. The programme covers recommendation engines, demand forecasting, pricing optimisation, customer analytics, and fulfilment efficiency in modern commerce environments.
Who should consider enrolling in this program?
The program is suitable for e-commerce and retail professionals, product and operations managers, data and analytics professionals, entrepreneurs and founders, and professionals working in delivery or hyperlocal platforms.
Does the program cover Generative AI applications in commerce?
Yes. The curriculum includes Generative AI in E-commerce use cases such as semantic search, automated customer support, content generation, and intelligent catalog interaction systems.
What certification will I receive after completion?
Participants who successfully complete the program receive a certificate from IIT Roorkee's Continuing Education Centre, recognised among advanced ecommerce certifications for applied AI learning.
Will I work on practical projects during the program?
Yes. Participants complete a full-stack capstone project where they apply AI techniques to solve a real commerce problem such as recommendation, churn prediction, or pricing optimisation.

Program Details

Duration 6 Months
Mode Online
Fees ₹90,000

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