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Introduction to Data Science and AI [1]

Paper Code: 
25CBDA111
Credits: 
03
Periods/week: 
03
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

Learning outcome

(at course level)

 

Learning and teaching strategies

 

Assessment

Strategies

 

Course

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Course

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25CBDA

111

 

 

 

 

 

 

 

 

 

 

 

 

 

Introducti on 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: In teractive Lectures, Dis cussion, Reading assignments, Demonstratio n.

 

Learning activities for

the student s: Self learning assig nments, Effective  que stions, 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

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 MarkovProcesses 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. 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 READINGS:

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 [2]

3.    https://www.geeksforgeeks.org/ [3]

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

 

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 [5]

Academic Year: 
2025-26 [6]

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Source URL: https://www.csit.iisuniv.ac.in/courses/subjects/introduction-data-science-and-ai-1

Links:
[1] https://www.csit.iisuniv.ac.in/courses/subjects/introduction-data-science-and-ai-1
[2] http://www.jupiter.com/
[3] http://www.geeksforgeeks.org/
[4] http://www.w3schools.com/python/default.asp
[5] http://www.springer.com/journal/13042
[6] https://www.csit.iisuniv.ac.in/academic-year/2025-26