IIT Delhi Certificate Programme in Applied Data Science & AI

The programme curriculum is structured across six progressive modules designed to take learners from foundational programming to professional-grade model deployment. Beginning with Python programming essentials including NumPy and Pandas, learners move into data optimisation techniques for machine learning workflows. The intermediate modules introduce supervised and unsupervised machine learning algorithms followed by deep learning architectures. This is complemented by applied industry cases from sectors such as healthcare, finance, e-commerce, and manufacturing. The final module addresses model deployment covering Docker, Flask, and AWS alongside best practices in MLOps. Assignments, case studies, and projects are integrated throughout and the programme concludes with a guided capstone project reinforcing end-to-end data science competency.

Module 1 covers Foundations of Python Programming introducing core programming concepts, data structures, and essential libraries including NumPy and Pandas. Learners build the coding fluency required to handle real datasets and prepare them for downstream ML workflows. Module 2 focuses on Optimising Data for ML Models addressing data cleaning, feature engineering, and preprocessing techniques critical to building effective machine learning pipelines. Module 3 introduces Machine Learning with Examples covering supervised and unsupervised learning algorithms with practical applications including classification, regression, clustering, and dimensionality reduction. Module 4 dives into Deep Learning exploring neural network architectures, backpropagation, CNNs, recurrent networks, reinforcement learning, and NLP fundamentals. Module 5 examines Applied Industry Cases presenting real-world AI applications across healthcare, finance, education, e-commerce, and manufacturing. Module 6 covers Applied Model Deployment and Special Topics equipping learners with skills in deploying trained models using Docker, Flask, and AWS with best practices in MLOps and a guided capstone project integrating all programme learnings.