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

**(True or False)** Time complexity has nothing to do with execution time. It’s all about the better algorithm. 


(True or False) Time complexity has nothing to do with execution time. It’s all about the better algorithm.

**(True or False)** Time complexity is important when we work with data structures.

(True or False) Time complexity is important when we work with data structures.

We can use Big O notation to compare two worst-case scenarios, which helps us decide which algorithm is better.

Big O notation doesn't show us time complexity.

We can use Big O notation to compare two best-case scenarios, which helps us decide which algorithm is better.

Which statement about Big O notation is true?

Which statement about Big O notation is true?

It represents a constant time complexity.

It represents a logarithmic time complexity.

It represents an exponential time complexity.

Which option best describes the time complexity value $n$?

Which option best describes the time complexity value <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi></mrow><annotation encoding="application/x-tex">n</annotation></semantics></math>n?

Which time complexity value represents a quadratic (second degree polynomial) time complexity?

Which time complexity value represents a quadratic (second degree polynomial) time complexity?

Test your understanding of time complexity and 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

Quiz: Time Complexity