Algorithmic Thinking with Python KTU S1 2024 scheme - course details and syllabus

SEMESTER S1

ALGORITHMIC THINKING WITH PYTHON (Common to All Branches)

B.Tech 2024 –S1/S2

Course Code: UCEST105 
Teaching Hours/Week (L: T:P: R) 3:0:2:0 
Credits 4 
ESE Marks 60
CIE Marks 40 
Exam Hours 2 Hrs. 30 Min
Prerequisites (if any) None 
Course Type :Theory

Course Objectives:

1. To provide students with a thorough understanding of algorithmic thinking and its practical applications in solving real-world problems.

2. To explore various algorithmic paradigms, including brute force, divide-and-conquer, dynamic programming, and heuristics, in addressing and solving complex problems.

Course Outcome and ÇO-PO mapping


SYLLABUS

Module-I
Contact
Hours- 7

PROBLEM-SOLVING STRATEGIES:- Problem-solving strategies defined, Importance of understanding multiple problem-solving strategies, Trial and Error, Heuristics, Means-Ends Analysis, and Backtracking (Working backward).
THE PROBLEM-SOLVING PROCESS:- Computer as a model of computation, Understanding the problem, Formulating a model, Developing an algorithm, Writing the program, Testing the program, and Evaluating the solution.
ESSENTIALS OF PYTHON PROGRAMMING:- Creating and using variables in Python, Numeric and String data types in Python, Using the math module, Using the Python Standard Library for handling basic I/O - print,input, Python operators and their precedence.

Module-II
Contact
Hours- 9

ALGORITHM AND PSEUDOCODE REPRESENTATION:- Meaning and Definition of Pseudocode, Reasons for using pseudocode, The main constructs of pseudocode - Sequencing, selection (if-else structure, case structure) and repetition (for, while, repeat-until loops), Sample problems*
FLOWCHARTS** :- Symbols used in creating a Flowchart - start and end, arithmetic calculations, input/output operation, decision (selection), module name (call), for loop (Hexagon), flow-lines, on-page connector, off-page connector.

* - Evaluate an expression, d=a+b*c, find simple interest, determine the larger of two numbers, determine the smallest of three numbers, determine the grade earned by a student based on KTU grade scale (using if-else and case structures), print the numbers from 1 to 50 in descending order, find the sum of n numbers input by the user (using all the three loop variants), factorial of a number, largest of n numbers (Not to be limited to these exercises. More can be worked out if time permits).

** Only for visualizing the control flow of Algorithms. The use of tools like RAPTOR (https://raptor.martincarlisle.com/) is suggested. Flowcharts for the sample problems listed earlier may be discussed.

Module-III
Contact
Hours- 10

SELECTION AND ITERATION USING PYTHON:- if-else, elif, for loop,range, while loop.Sequence data types in Python - list, tuple, set, strings, dictionary, Creating and using Arrays in Python (using Numpy library).
DECOMPOSITION AND MODULARIZATION* :- Problem decomposition as a strategy for solving complex problems, Modularization, Motivation for modularization, Defining and using functions in Python, Functions with  multiple return values
RECURSION:- Recursion Defined, Reasons for using Recursion, The Call Stack,Recursion and the Stack, Avoiding Circularity in Recursion, Sample problems -Finding the nth Fibonacci number, greatest common divisor of two positive integers, the factorial of a positive integer, adding two positive integers, the sum of digits of a positive number **.

* The idea should be introduced and demonstrated using Merge sort, the problem of returning the top three integers from a list of n>=3 integers as examples. (Not to be limited to these two exercises. More can be worked out if time permits).
** Not to be limited to these exercises. More can be worked out if time permits.

Module-IV
Contact
Hours- 10

COMPUTATIONAL APPROACHES TO PROBLEM-SOLVING
(Introductory diagrammatic/algorithmic explanations only. Analysis not required) :-
Brute-force Approach -
- Example: Padlock, Password guessing
Divide-and-conquer Approach -
- Example: The Merge Sort Algorithm
- Advantages of Divide and Conquer Approach
- Disadvantages of Divide and Conquer Approach
Dynamic Programming Approach
- Example: Fibonacci series
- Recursion vs Dynamic Programming
Greedy Algorithm Approach
- Example: Given an array of positive integers each indicating the completion time for a task, find the maximum number of tasks that can be completed in the limited amount of time that you have.
- Motivations for the Greedy Approach
- Characteristics of the Greedy Algorithm
- Greedy Algorithms vs Dynamic Programming
Randomized Approach

- Example 1: A company selling jeans gives a coupon for each pair of jeans.There are n different coupons. Collecting n different coupons would give you free jeans. How many jeans do you expect to buy before getting a free one?

- Example 2: n people go to a party and drop off their hats to a hat-check person.When the party is over, a different hat-check person is on duty and returns then hats randomly back to each person. What is the expected number of people who get back their hats?
- Motivations for the Randomized Approach

Video Reference



1. Continuous Assessment (5 Marks) 
   Accurate Execution of Programming Tasks 
   Correctness and completeness of the program 
   Efficient use of programming constructs 
   Handling of errors 
   Proper testing and debugging 
2. Evaluation Pattern for Lab Examination (10 Marks) 
a. Algorithm (2 Marks) 
    Algorithm Development: Correctness and efficiency of the algorithm related to the question. 
b. Programming (3 Marks) 
    Execution: Accurate execution of the programming task. 
c. Result (3 Marks) 
    Accuracy of Results: Precision and correctness of the obtained results. 
d. Viva Voce (2 Marks) 
    Proficiency in answering questions related to theoretical and practical aspects of the subject.

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