# The Complete Mathematics Software Developer Course for 2022

All Mathematics You Need To Know As a Programmer

Language: english

Note: 4.7/5 (138 notes) 39,608 students

Instructor(s): Martin Yanev

Last update: 2021-05-30

## What you’ll learn

• Proof Techniques. Mathematical Induction and Recursion Theory.
• Mathematical Logic. Propositional and First Order Calculus. Model Theorem.
• Programs verifications and Model Checking
• Linear Algebra. Matrix Theory in Computer Science.
• Boolean Algebra and its applications in Digital Electronics.
• Lambda Calculus as a Foundation of Functional Programming
• Number Theory and Encryption.
• Modern Statistics and Probabilistic Methods in Computer Science.
• Functional Analysis and the efficiency of computer algorithms Decision Theory

## Requirements

• Desire to Learn Mathematics for Programming
• Interested in Computer Science Field
• Basic High School Mathematics

## Description

This course covers all Mathematics needed to become Software Developer. Here we will discuss Linear Algebra, Modern Analysis, Mathematical Logic, Number Theory and Discrete Mathematics. By the end of this course you will be able to analyze and describe computer science concepts and methods. This course is a great opportunity for you to gain deep understanding of all processes a executed in the computer system when programming. The specific objectives of the course are the following:

1. Learn how to apply proof techniques to your computer program.

2. Learn encrypting and decrypting messages with Number Theory.

3. Learn how the software development is related to Discrete Mathematics and Digital Electronics.

4. Understand how to use mathematical tools to properly analyze any computer algorithm.

5. Learn how to apply Calculus, Probability Theory and Linear Algebra while computing.

6. Understand how to apply Lambda Calculus to Functional Programming.

Discrete mathematics is the study of mathematical structures that are fundamentally discrete rather than continuous. In contrast to real numbers that have the property of varying “smoothly”, the objects studied in discrete mathematics – such as integers, graphs, and statements in logic – do not vary smoothly in this way, but have distinct, separated values.Discrete mathematics therefore excludes topics in “continuous mathematics” such as calculus or Euclidean geometry. Discrete objects can often be enumerated by integers. More formally, discrete mathematics has been characterized as the branch of mathematics dealing with countable sets (finite sets or sets with the same cardinality as the natural numbers). However, there is no exact definition of the term “discrete mathematics.” Indeed, discrete mathematics is described less by what is included than by what is excluded: continuously varying quantities and related notions.

## Who this course is for

• Beginner Java Developers
• Beginner Python Developers
• Beginner C & C++ Developers
• Computer Science Students
• Engineering Students
• Employees in Programming Companies

## Course content

• Introduction
• Introduction
• Why Learning Mathematics for Computer Science?
• Boolean Variables Logic
• Boolean Variables
• Truth Tables
• De Morgan’s Law
• Boolean Exercise – Solution
• Boolean Algebra for Digital Electronics
• Boolean Operations in Computer Hardware
• Computer Transistors and Gates
• Circuit Representation and Exercise
• Circuit Representation: Exercise Solution
• Simplification of Logical Circuits
• Set Reset Flip – Flop
• Logical Circuits and SR Flip-Flop: Exercise Solution
• Numerical Systems and Their Applications
• Decimal Numerical System
• Binary Numerical System
• Two’s Component Notation
• Digital Representations and Error Detection
• Representation of Characters and Numerical Values
• Digital Representation of Sounds
• Digital Representation of Images
• Error-Correction in the Digital Systems
• Set Theory
• Sets Relations
• Operations With Sets
• Set Theory Relations
• Finite Automata
• Theory of Computation
• Finite Automata
• Deterministic Finite Automata (DFA)
• DFA Challenge
• Non – Deterministic Finite Automata & Regular Operations
• Non – Deterministic Finite Automata
• NFA Examples: Practical Exercise
• Operations With Languages
• Regular Languages
• Regular Expressions
• Numbers Theory
• Divisability
• Euclidean Algorithm
• Modular Arithmetic
• Prime Number Functions
• Prime Number Testing
• Cyber Security: Public Key Cryptography
• Encryption and Decryption of Public Keys
• Encryption and Decryption of Schemes
• Key Generation with RSA: Practical Exercise
• RSA Exercise Solution
• Key Exchange Algorithm of Diffie – Hellman
• Key Exchange Algorithm: Exercise Solution
• Dijkstra Algorithm
• Dijkstra Algorithm | Part 1
• Dijkstra Algorithm | Part 2
• Bonus Lecture
• Bonus Lecture  Time remaining or 370 enrolls left

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