INTRODUCTION TO DATA SCIENCE AND AI

Paper Code: 
CBDA 111
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 
  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 (COs). 

   Course Outcome (at course level)

Learning and       teaching strategies

Assessment Strategies

On completion of this course, the students will:

CO1. Analyze the mathematical concepts of data science to frame and compute an abstract of the business problem.

CO2. Differentiate between structured and unstructured data.

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

CO4. Develop State Space Search for different problems in AI.

CO5. Identify different learning approaches in AI.

 

Approach in teaching:

Interactive Lectures, Group Discussion, Tutorials, Case Study, Demonstration

 

Learning activities for the students:

Self-learning assignments, presentations, practical exercise

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

 

9.00
Unit I: 

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 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 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 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.

 

REFERENCES: 
1. McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
2. Curtis Miller, ”Hands-On Data Analysis with NumPy and Pandas",Packt, 2015
 
 
 
Academic Year: 
2022-23
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