DSxxx Introduction to Data Science & Artificial Intelligence

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: Foundations of Data Science

Lecture 2: Data Collection, Cleaning, Storage and Integration

Lecture 3: Data Analysis and Inference

Lecture 4: Foundations of AI

Lecture 5: Knowledge Representation

Lecture 6: Principles of Problem Solving using State Space Search

Lecture 7: Introduction to AI Subfields

Self-Study 1: 

Self-Study 2:

Self-Study 3: 

Self-Study 4: 

Self-Study 5: 

Self-Study 6: 

“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)

7 Quizzes: 42%

2 Assignments: 20%

Midsem: 20%

Attendance: 18%