Introduction to Data Science and AI (Theory)

Paper Code: 
24CBDA111
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

The course will enable the students to:

  1. Develop an understanding of the role of computation in solving problems.
  2. Understand the importance of Data science with real life examples.
  3. Describe the fundamentals of AI and its applications.

 

Course Outcomes: 

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Course Code

Course Title

 

24CBDA111

 

Introduction to Data Science and AI

 (Theory)

CO1. Analyse the mathematical concepts of data science to formulate and compute an abstract model of the real world scenario.

CO2. Assess data using exploratory analysis and differentiate between structured and unstructured data.

CO3. Develop detailed step-by-step solutions to problems, interpret data, and understand how different data extraction and data mining techniques improve problem solution efficiency.

CO4. Discuss the concept of Artificial Intelligence and develop a State Space Search for different problems in AI.

CO5. Identify different learning approaches in AI and ethical consideration in AI.

CO6 Contribute effectively in

course-specific interaction.

 

Approach in teaching: Interactive Lectures, Discussion, Reading assignments, Demonstration.

 

Learning activities for the students: Self learning assignments, Effective questions, Seminar presentation.

 

Class test, Semester end examinations, Quiz, Assignments, Presentation.

 

9.00
Unit I: 
Introduction to Data Science and problem solving approach

Data-Science: What is Data Science? – The core problems and solutions. Extracting Intelligence from Data – formulating problems, The Data Pipeline Types of Data in various practical Data Science scenarios. Data Wrangling, Cleaning and Preparation. Data Science Lifecycle. Career Opportunities in Data Science.

Problem Solving and Algorithmic Thinking: Problem definition, Logical reasoning, Problem decomposition, Abstraction. Flowcharting, Name binding, Selection, Repetition, Modularization.

 

9.00
Unit II: 
Data Presentation and Exploratory Analysis

Basic concepts in Statistics and Exploratory Data Analysis. Data Exploration and Data Visualization. Case Studies involving Structured and Unstructured Data

 

9.00
Unit III: 
Data extraction and data mining

Data extracting, pattern recognition, Data mining and its task classification, prediction, association, clustering and dimension reduction. Application of data mining, Performance Analysis.

 

9.00
Unit IV: 
Artificial Intelligence

Artificial Intelligence What is Artificial Intelligence? – History and State-of-Art. Principles of problem solving and the State Space Search. Case Studies for State Space Search and Search Algorithms

 

9.00
Unit V: 
Reinforcement Learning and AI

Introduction to Reinforcement Learning in context of AI. Fundamentals of Markov Processes and Q-Learning. Ethics in DS&AI Ethical considerations and the idea of responsible DS & AI.

 

ESSENTIAL READINGS: 

 

  1. Karl Beecher,”Computational Thinking: A beginner's guide to problem-solving and programming”,BCS Learning & Development Limited,2017.
  2. Madhavan, “Mastering Python for Data Science”, Packt, 2015.
  3. Mahankali, Srinivas., Srivastava, Amitendra., Cuddapah, Vijay.
  4. SRIVASTAVA. AI & ML - Powering the Agents of Automation: Demystifying, IOT, Robots, ChatBots, RPA, Drones & Autonomous Cars- The New Workforce Led Digital Reinvention Facilitated by AI & ML and Secured Through Blockchain. India: BPB Publications, 2019.
 
REFERENCES: 

SUGGESTED REFERENCE BOOKS

1.  McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.

2.  Miller, Curtis. Hands-On Data Analysis with NumPy and Pandas: Implement Python Packages from Data Manipulation to Processing. United Kingdom: Packt Publishing, 2018.

 

e-RESOURCES:

1.     NOC: Python for Data Science, IIT Madras ,https://nptel.ac.in/courses/106106212

2.     Jupiter :www.jupiter.com

3.     https://www.geeksforgeeks.org/

4.     https://www.w3schools.com/python/default.asp

 

JOURNALS:

1.      Journal of Machine Learning Research (JMLR),ACM, https://dl.acm.org/journal/jmlr

2. International Journal of Machine Learning and Cybernetics, springer : https://www.springer.com/journal/13042 

Academic Year: 
2024-25
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