DS161 Introduction to Data Science & Artificial Intelligence

Lecture 0

Introducing Course

Krishnendu Ghosh

Course Details

Credit: 2 (1.5-0-0-2-2)

​

Course: Introduction to Data Science & Artificial Intelligence

​

Course Type: Core

Course Overview

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.

Course Overview

• 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.

Syllabus

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?

Syllabus

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

Syllabus

Unit 3: Introduction to AI Application & its subfields

​

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

Text books

1. “Data Science” by John D Kelleher, Brendan Tierney, MIT Press, 2018, ISBN: 9780262347037 (For Unit 2)

​

2. “Artificial Intelligence: A Modern Approach” by Russell and Norvig (4th edition) , Pearson , 2020, ISBN: 978-0134610993 (For Unit 3)

​

​

Grading Policy

​

Endsem 30%

​

Quiz 20%

​

2 Assignments 40%

​

Attendance 10%

Timeline

​

Endsem Notified by Exam Cell

​

Quiz Each class, 15 Students

​

Assignment 1 September 28

​

Assignment 2 October 23