Gain insights into connecting discrete mathematics with programming. Explore variables, expressions, data structures, and algorithm design to enhance your problem-solving and implementation skills.

Discrete Mathematical Algorithms and Data Structures-02-02.png

momoexp4.tar.gz

java and C++

OnlyPython

OnlyJava

OnlyCplusplus

OnlyC

JavaLang3

Discrete mathematics is the basis of several popular algorithms. This course shows the discreteness of data structures and algorithms. It can benefit programmers with a non-CS background looking to strengthen their foundations by connecting theoretical concepts with practical programming scenarios.

The course starts with discussing relating discrete mathematics with algorithms and data structures. You will have examples demonstrating variables, conditional expressions, and arrays as basic building blocks of programming. Then, you will use classes and objects to define and implement linear data structures. You will also learn about the binary tree traversals and templates and the topics of time complexity, set difference, and rearranging the values in a string.

By the end of the course, you will have a strong grasp of algorithm design, data structure implementation, and overall problem-solving.

Programming Discrete Math Concepts for Beginners

# Standard notation of time complexity

We know how we can represent any algorithm through the order of $n$ or input. 
Big O notation is the most convenient way to express the worst-case scenario for an algorithm. 

Consider a code where we use the `sqrt(Range)` algorithm. The Big O notation is $O(\sqrt{n})$ for this case. This algorithm couldn’t be worse than anything.

Consider another code where we have to iterate over one million times if $n$ is 1000003. The Big O notation is $O(n)$ for this case, which is the worst-case scenario in this case.

Now, comparing these two worst-case scenarios, we can safely say that the algorithm of the former is better than the latter.

Review an in-depth guide to Big O notation.

Course Introduction

Programming Language and Boolean Algebra

Logical Expressions and Algorithms

Arrays and Discrete Mathematics

Classes, Objects, and Methods

Linear Data Structures

Tree, Algorithms, and Templates

Time Complexity

Propositional Logic

Combinatorics

Wrapping Up

Big O Notation

Standard notation of time complexity