Class Timing: Monday (10.45-12.15) and Thursday (9.00-10.30) at C202
Self-study Timing: Tuesday (12.15-13.15)
Assignments:
Assignment 1 on AI, Due Date: September 28, 2026; Submission Link
Assignment 2, Date: October 23, 2026; Live AI/DS Problem Solving by Team of 5; Submit Team by September 14, 2026
Endsem: ?? October, 2026; Syllabus: upto Lecture 5
Quiz: Each class, 15 Students need to answer 2 questions
Course Objectives
This course provides a broad overview of the exciting and rapidly evolving fields of Data Science and Artificial Intelligence. Students will gain foundational knowledge of the core concepts, techniques, and applications of both domains. This gives the flavor of DS and AI to the students across different disciplines.
Learning Outcomes
LO1: Explain the core concepts, history, and significance of Data Science and Artificial Intelligence.
LO2: Demonstrate knowledge of data collection methods, data types, and preprocessing techniques, including data quality and cleaning.
LO3: Explain the foundations of AI, including its subfields, infrastructure, and identify its applications across various domains.
LO4: Describe key terminologies and algorithms in Machine Learning, and discuss the ethical implications of AI.
Unit 1: Introduction
Foundations of Data Science- Data, Information, Knowledge and Wisdom Pyramid- Data Science vs Artificial Intelligence : analysis and applications - Past, present and future of DS & AI - Case study: How DS & AI impacts across various disciplines?
Unit 2: Data Representation & Inference
Data collection methods and sources - Data types and formats structured, unstructured, semi-structured, numerical, categorical, image, audio, sensor, time-series, etc - Introduction to data quality and cleaning : GIGO principle- Introduction to data storage and integration: Database vs Data Warehouse - Data Analysis and Inference: Descriptive - Exploratory - Diagnostic - Predictive - Prescriptive - Data driven decision making - Causal inference - Case study with Hands-on: Data cleaning frameworks using OpenRefine
Unit 3: Introduction to AI Application & its subfields
History of AI - Evolution of AI: Rule based system to Generative AI- AI - Tools, frameworks and infrastructure- Rise of GPUs- Knowledge representation- Ontology- Principles of problem solving and the state space search- Types of AI based on capabilities and functionalities- Introduction to AI subfields: Expert system- Machine learning- Deep learning- NLP - Computer Vision- Reinforcement learning- AI Ethics: Algorithmic bias and fairness - Case study: Impact of deep fake and ethical consideration of AI
Lecture 0: Course Introduction
Lecture 1: Introduction to Artificial Intelligence: From Rules to Generative AI
Lecture 2: Problem Solving using State-Space Search I
Lecture 3: Problem Solving using State-Space Search II
Lecture 4: Knowledge Representation
Lecture 5: Introduction to AI Subfields
Lecture 6: Introduction to Data Science: From Data to Decisions
Lecture 7: Data Sources, Types and Representation
Lecture 8: Data Quality, Cleaning and Integration
Lecture 9: Data Analysis and Inference
Self-Study 1: AI Around Us: Identifying Intelligent Systems
Self-Study 2: AI Evolution: From Expert Systems to Generative AI
Self-Study 3: Search Algorithms in Action
Self-Study 4: Heuristics and A* Search
Self-Study 5: Knowledge Representation in the Real World
Self-Study 6: Exploring and Understanding Real-World Data
Self-Study 7: Data Cleaning with OpenRefine
“Data Science” by John D Kelleher, Brendan Tierney, MIT Press, 2018, ISBN: 9780262347037 (For Unit 2)
“Artificial Intelligence: A Modern Approach” by Russell and Norvig (4th edition) , Pearson , 2020, ISBN: 978-0134610993 (For Unit 3)
Quiz: 20%
2 Assignments: 40%
Endsem: 30%
Attendance: 10%