# Essential Algorithms: A Practical Approach to Computer Algorithms Using Python and C#

(ESS-ALGO.AE1)/ISBN:978-1-64459-270-0

This course includes
Lessons
TestPrep
Lab

Enroll yourself in the Essential Algorithms: A Practical Approach to Computer Algorithms Using Python and C# course and lab to learn algorithms techniques. The course and lab provide expertise over the concepts such as algorithms, linked lists, arrays, stacks and queues, sorting, searching, hash tables, recursion, trees, cryptography, complexity theory, interview puzzles, and more.

#### Lessons

21+ Lessons | 168+ Quizzes | 434+ Flashcards | 436+ Glossary of terms

#### TestPrep

60+ Pre Assessment Questions | 57+ Post Assessment Questions |

# Here's what you will learn

### Lessons 1: Introduction

• Why You Should Study Algorithms
• Algorithm Selection
• Who This Course Is For
• Getting the Most Out of This Course
• How This Course Is Structured
• What You Need to Use This Course
• Conventions

### Lessons 2: Algorithm Basics

• Approach
• Algorithms and Data Structures
• Pseudocode
• Algorithm Features
• Practical Considerations
• Summary
• Exercises

### Lessons 3: Numerical Algorithms

• Randomizing Data
• Finding Greatest Common Divisors
• Performing Exponentiation
• Working with Prime Numbers
• Performing Numerical Integration
• Finding Zeros
• Gaussian Elimination
• Least Squares Fits
• Summary
• Exercises

• Basic Concepts
• Summary
• Exercises

### Lessons 5: Arrays

• Basic Concepts
• One-Dimensional Arrays
• Nonzero Lower Bounds
• Triangular Arrays
• Sparse Arrays
• Matrices
• Summary
• Exercises

### Lessons 6: Stacks and Queues

• Stacks
• Queues
• Binomial Heaps
• Summary
• Exercises

### Lessons 7: Sorting

• O(N2) Algorithms
• O(N log N) Algorithms
• Sub O(N log N) Algorithms
• Summary
• Exercises

### Lessons 8: Searching

• Linear Search
• Binary Search
• Interpolation Search
• Majority Voting
• Summary
• Exercises

### Lessons 9: Hash Tables

• Hash Table Fundamentals
• Chaining
• Summary
• Exercises

### Lessons 10: Recursion

• Basic Algorithms
• Factorial
• Fibonacci Numbers
• Rod-Cutting
• Tower of Hanoi
• Graphical Algorithms
• Koch Curves
• Hilbert Curve
• Sierpiński Curve
• The Skyline Problem
• Backtracking Algorithms
• Eight Queens Problem
• Knight's Tour
• Selections and Permutations
• Selections with Loops
• Selections with Duplicates
• Selections Without Duplicates
• Permutations with Duplicates
• Permutations Without Duplicates
• Round-Robin Scheduling
• Recursion Removal
• Tail Recursion Removal
• Dynamic Programming
• Bottom-Up Programming
• General Recursion Removal
• Summary
• Exercises

### Lessons 11: Trees

• Tree Terminology
• Binary Tree Properties
• Tree Representations
• Tree Traversal
• Sorted Trees
• Lowest Common Ancestors
• Specialized Tree Algorithms
• Interval Trees
• Summary
• Exercises

### Lessons 12: Balanced Trees

• AVL Trees
• 2-3 Trees
• B-Trees
• Balanced Tree Variations
• Summary
• Exercises

### Lessons 13: Decision Trees

• Searching Game Trees
• Searching General Decision Trees
• Swarm Intelligence
• Summary
• Exercises

### Lessons 14: Basic Network Algorithms

• Network Terminology
• Network Representations
• Traversals
• Strongly Connected Components
• Finding Paths
• Transitivity
• Shortest Path Modifications
• Summary
• Exercises

### Lessons 15: More Network Algorithms

• Topological Sorting
• Cycle Detection
• Map Coloring
• Maximal Flow
• Network Cloning
• Cliques
• Community Detection
• Eulerian Paths and Cycles
• Summary
• Exercises

### Lessons 16: String Algorithms

• Matching Parentheses
• Pattern Matching
• String Searching
• Calculating Edit Distance
• Phonetic Algorithms
• Summary
• Exercises

### Lessons 17: Cryptography

• Terminology
• Transposition Ciphers
• Substitution Ciphers
• Block Ciphers
• Public-Key Encryption and RSA
• Other Uses for Cryptography
• Summary
• Exercises

### Lessons 18: Complexity Theory

• Notation
• Complexity Classes
• Reductions
• 3SAT
• Bipartite Matching
• NP-Hardness
• Detection, Reporting, and Optimization Problems
• Detection ≤p Reporting
• Reporting ≤p Optimization
• Reporting ≤p Detection
• Optimization ≤p Reporting
• Approximate Optimization
• NP-Complete Problems
• Summary
• Exercises

### Lessons 19: Distributed Algorithms

• Types of Parallelism
• Distributed Algorithms
• Summary
• Exercises

• Summary
• Exercises

### Appendix A: Summary of Algorithmic Concepts

• Lesson 1: Algorithm Basics
• Lesson 2: Numeric Algorithms
• Lesson 4: Arrays
• Lesson 5: Stacks and Queues
• Lesson 6: Sorting
• Lesson 7: Searching
• Lesson 8: Hash Tables
• Lesson 9: Recursion
• Lesson 10: Trees
• Lesson 11: Balanced Trees
• Lesson 12: Decision Trees
• Lesson 13: Basic Network Algorithms
• Lesson 14: More Network Algorithms
• Lesson 15: String Algorithms
• Lesson 16: Cryptography
• Lesson 17: Complexity Theory
• Lesson 18: Distributed Algorithms
• Lesson 19: Interview Puzzles

# Hands-on LAB Activities (Performance Labs)

### Algorithm Basics

• Discussing about Algorithms, Numerical Algorithms, and Arrays
• Learning Common Run Time Functions
• Understating about Big O Notation

### Numerical Algorithms

• Creating Pseudorandom Numbers
• Making Random Walks
• Calculating Greatest Common Divisors
• Testing of Primality
• Performing Numerical Integration
• Using Back Substitution

• Finding Cells

### Arrays

• Discussing about Arrays, Stacks and Queues, and Sorting
• Finding Median
• Finding Average

### Stacks and Queues

• Reversing An Array
• Understanding Stacks
• Understanding Queues
• Merging Trees
• Understanding Binomial Trees

### Sorting

• Understanding the Heap Sort Algorithm
• Understanding Sorting Algorithm
• Summarizing the Algorithms

### Searching

• Understanding the Linear Search Algorithm
• Understanding Binary Search
• Understanding Interpolation Search
• Discussing about Searching, Hash Tables, and Recursion

### Recursion

• Understanding the Factorial
• Learning about the Koch Curves
• Understanding Eight Queens Problem

### Trees

• Understanding about Balanced and Decision Trees
• Understanding Tree Terminology
• Calculating Number of Nodes

### Balanced Trees

• Deleting Values

### Decision Trees

• Understanding Random Search

### Basic Network Algorithms

• Understanding Network Terminology

### More Network Algorithms

• Using the Brute Force Approach

### String Algorithms

• Understanding Pattern Matching
• Discussing about Network and String Algorithms

### Cryptography

• Calculating the Euler's Totient Function

### Distributed Algorithms

• Discussing about Cryptography, Complexity Theory, and Distributed Algorithms