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Essential Algorithms Using Python and C#

Explore computer algorithms for solving real-world problems using Python and C# programming language.

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

This course is about solving real-world problems following the principles of computer algorithms using Python and C# languages. Learn the fundamentals of Python algorithms (step-by-step instructions for solving problems) and data structures (organizing, storing, and accessing data). Explore a wide range of structures including arrays, linked lists, stacks, and queues. Learn how to measure an algorithm's most crucial complexities including how long it takes to run (time) and how much space it uses (space/memory). The course includes exercises and solutions that are useful for both students and professionals. It aims to help you prepare for programming job interviews that typically involve technical algorithm questions.

Skills You’ll Get

  • Create algorithms to solve computational problems.
  • Implementing data structures for storing and accessing data
  • Expertly handling sorting algorithms like sort, merge sort, quicksort, and binary search
  • Determining algorithm’s space and time complexities
  • Learning advanced algorithms techniques like recursion, backtracking, and divide & conquer
  • Gain confidence to answer computer algorithm related technical questions during job interviews
  • Enhance your programming skills in both Python and C#
Download Course Outline

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
2

Algorithm Basics

  • Approach
  • Algorithms and Data Structures
  • Pseudocode
  • Algorithm Features
  • Practical Considerations
  • Summary
  • Exercises
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
4

Linked Lists

  • Basic Concepts
  • Singly Linked Lists
  • Doubly Linked Lists
  • Sorted Linked Lists
  • Self-Organizing Linked Lists
  • Linked-List Algorithms
  • Multithreaded Linked Lists
  • Linked Lists with Loops
  • Summary
  • Exercises
5

Arrays

  • Basic Concepts
  • One-Dimensional Arrays
  • Nonzero Lower Bounds
  • Triangular Arrays
  • Sparse Arrays
  • Matrices
  • Summary
  • Exercises
6

Stacks and Queues

  • Stacks
  • Queues
  • Binomial Heaps
  • Summary
  • Exercises
7

Sorting

  • O(N2) Algorithms
  • O(N log N) Algorithms
  • Sub O(N log N) Algorithms
  • Summary
  • Exercises
8

Searching

  • Linear Search
  • Binary Search
  • Interpolation Search
  • Majority Voting
  • Summary
  • Exercises
9

Hash Tables

  • Hash Table Fundamentals
  • Chaining
  • Open Addressing
  • Summary
  • Exercises
10

Recursion

  • Basic Algorithms
  • Factorial
  • Fibonacci Numbers
  • Rod-Cutting
  • Tower of Hanoi
  • Graphical Algorithms
  • Koch Curves
  • Hilbert Curve
  • Sierpiński Curve
  • Gaskets
  • 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
11

Trees

  • Tree Terminology
  • Binary Tree Properties
  • Tree Representations
  • Tree Traversal
  • Sorted Trees
  • Lowest Common Ancestors
  • Threaded Trees
  • Specialized Tree Algorithms
  • Interval Trees
  • Summary
  • Exercises
12

Balanced Trees

  • AVL Trees
  • 2-3 Trees
  • B-Trees
  • Balanced Tree Variations
  • Summary
  • Exercises
13

Decision Trees

  • Searching Game Trees
  • Searching General Decision Trees
  • Swarm Intelligence
  • Summary
  • Exercises
14

Basic Network Algorithms

  • Network Terminology
  • Network Representations
  • Traversals
  • Strongly Connected Components
  • Finding Paths
  • Transitivity
  • Shortest Path Modifications
  • Summary
  • Exercises
15

More Network Algorithms

  • Topological Sorting
  • Cycle Detection
  • Map Coloring
  • Maximal Flow
  • Network Cloning
  • Cliques
  • Community Detection
  • Eulerian Paths and Cycles
  • Summary
  • Exercises
16

String Algorithms

  • Matching Parentheses
  • Pattern Matching
  • String Searching
  • Calculating Edit Distance
  • Phonetic Algorithms
  • Summary
  • Exercises
17

Cryptography

  • Terminology
  • Transposition Ciphers
  • Substitution Ciphers
  • Block Ciphers
  • Public-Key Encryption and RSA
  • Other Uses for Cryptography
  • Summary
  • Exercises
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
19

Distributed Algorithms

  • Types of Parallelism
  • Distributed Algorithms
  • Summary
  • Exercises
20

Interview Puzzles

  • Asking Interview Puzzle Questions
  • Answering Interview Puzzle Questions
  • Summary
  • Exercises
A

Appendix A: Summary of Algorithmic Concepts

  • Lesson 1: Algorithm Basics
  • Lesson 2: Numeric Algorithms
  • Lesson 3: Linked Lists
  • 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

1

Algorithm Basics

  • Discussing about Algorithms, Numerical Algorithms, and Arrays
  • Learning Common Run Time Functions
  • Understating about Big O Notation
2

Numerical Algorithms

  • Creating Pseudorandom Numbers
  • Making Random Walks
  • Calculating Greatest Common Divisors
  • Testing of Primality
  • Performing Numerical Integration
  • Using Back Substitution
3

Linked Lists

  • Finding Cells
4

Arrays

  • Discussing about Arrays, Stacks and Queues, and Sorting
  • Finding Median
  • Finding Average
  • Learning about Array Types
  • Adding Matrices
5

Stacks and Queues

  • Reversing An Array
  • Understanding Stacks
  • Understanding Queues
  • Merging Trees
  • Understanding Binomial Trees
6

Sorting

  • Understanding the Heap Sort Algorithm
  • Understanding Sorting Algorithm
  • Summarizing the Algorithms
7

Searching

  • Understanding the Linear Search Algorithm
  • Understanding Binary Search
  • Understanding Interpolation Search
  • Discussing about Searching, Hash Tables, and Recursion
8

Hash Tables

  • Understanding Open Addressing
9

Recursion

  • Understanding the Factorial
  • Learning about the Koch Curves
  • Understanding Eight Queens Problem
10

Trees

  • Understanding about Balanced and Decision Trees
  • Understanding Tree Terminology
  • Calculating Number of Nodes
  • Learning About Tree Traversal
11

Balanced Trees

  • Deleting Values
12

Decision Trees

  • Understanding Random Search
13

Basic Network Algorithms

  • Understanding Network Terminology
14

More Network Algorithms

  • Using the Brute Force Approach
15

String Algorithms

  • Understanding Pattern Matching
  • Discussing about Network and String Algorithms
16

Cryptography

  • Calculating the Euler's Totient Function
17

Distributed Algorithms

  • Discussing about Cryptography, Complexity Theory, and Distributed Algorithms

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It is an advanced level course that offers an all-inclusive study on Essential Algorithms. Knowledge of basic programming language and familiarity with elementary mathematical concepts like arithmetic and logarithms will help you understand better. If you don’t have considerable experience, you can always look up and study a bit to brush up your skills before starting the course.

Yes, you’ll be learning both the programming languages.

This course is designed for a broad spectrum of users who want to improve their problem-solving skills using algorithms. It is an ideal course for strengthening your programming skills and writing more efficient and optimized codes. All those wanting to gain a competitive-edge in technical round interviews can benefit greatly from this course.

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