Algorithmic Thinking with Python UCEST 105- KTU First Semester BTech Course 2024 scheme notes pdf - Dr Binu V P 9847390760

About Me- Dr Binu V P, Head of the Department -CS,Model Engineering College -IHRD, Cochin
Algorithmic Thinking with Python KTU S1 2024 scheme - course details and syllabus

Algorithmic Thinking with Python - Textbook

About NASSCOM Digital 101 Course - KTU 2024 Scheme

Model Question Paper UCEST 105 Algorithmic Thinking with Python-University

UCEST 105 Algorithmic Thinking with Python - University Model Question Paper and Answer Key

UCEST 105 Algorithmic Thinking with Python - KTU University Question Paper and Answer Key

Why Python
Introduction to Python
How to work with Python

Sample Class Room Exercises

Laboratory Details

Basic Linux Commands

Lab Experiments and Solutions ( mandatory programs)

Lab Record

Lab Cycle-1

Lab Cycle-2

Lab Cycle-3

Lab Cycle-4

Lab Cycle-5

Lab Cycle-6

Module-I

Problem and Computer Programming Problem

Problem Solving Strategies

Heuristic Method

Heuristic Method- Examples ( tutorial discussion)

Trial and Error

Trial and Error- Examples ( tutorial discussion)
Means-End Analysis

Means-End Analysis- Examples(tutorial discussion)

Backtracking

Backtracking-Examples(tutorial discussion)

Problem Solving Process

Essentials of Python Programming

Identifiers, variables and keywords

Basic Data Types and Type Conversions
Operators
Operator Precedence and Expression Evaluation
input() and print() functions
Built-in functions, Modules and Packages

Simple programs to begin with comments,statements,IDEs
Strings

Errors in Python Program

Lab Programs ( Contact for More Programs)

Running Python Programs IDLE, Jupiter Notebook

Basic Programs to Try

Programs using built-in functions and modules/packages

Programs to try using strings

Module- II

Algorithm and Pseudo code

Program Development

Introduction to Algorithmic Thinking

Algorithms

Pseudo Code

Flow Chart

Flow Chart Symbols and meaning

Algorithm, Flowchart and Pseudo code-Examples
Introduction to RAPTOR (Rapid Algorithmic Prototyping Tool for Ordered Reasoning)

Lab

RAPTOR is a flowchart-based programming environment, designed specifically to help students visualize their algorithms and avoid syntactic baggage. RAPTOR programs are created visually and executed visually by tracing the execution through the flowchart.(Download RAPTOR)

Introduction to Programming with RAPTOR

RAPTOR Overview

RAPTOR Examples to Try

Module - III

Control Statements- Selection and Iteration

Conditional statements if,if-elif, switch

Looping (iteration )statements- while and for

Nested Loops

break continue and pass statements

Example Programs using loops and nested loops(university questions)

Data structures

Sequences in Python

Lists in Python

List Functions/methods

Example Programs using Lists ( university questions)

Tuples in Python

Sets in Python

Set Functions/Frozen sets

Example Programs using Set ( university questions)

Strings in Python

Example Programs using strings ( university questions)

Dictionaries in Python

Dictionary Functions

Example Program using Dictionary ( university questions)

Comparison List, tuple, set, dictionary

numpy arrays

numpy matrices

Lab

Programs to try using if-else,if-elif

Programs to try using loops

Programs to try using Lists

Programs to try using tuples and set

Programs to try using Dictionary

Programs to try using Strings

Decomposition and Modularization-Functions in Python

Problem Decomposition-Modularization

Python Anonymous/lambda Function
Defining and calling functions in Python
Global and local variables
Example programs using functions(university questions)

Recursion

The Call Stack

Avoiding Circularity in Recursion

Example Programs using recursion(university questions)

Iteration Vs Recursion

Lab

Programs to try using functions

Programs to try using recursion

Module-IV 

Computational Approaches to Problem Solving

Introduction 
Brute-force Approach 

    Padlocking

    Password Guessing
Divide-and-conquer Approach-Merge Sort
Dynamic Programming Approach

   Fibonacci Series

   Recursion Vs Dynamic Programming
Greedy Algorithm Approach

   Examples of Greedy Algorithm
   Motivations for the Greedy Approach
   Characteristics of the Greedy Algorithm
   Greedy Algorithms vs Dynamic Programming

Randomized  Algorithm Approach

    Motivation for randomized algorithms

    Coupon and Hat Problem

    Random Sampling

Lab

Solve problems using various computational approaches