Introduction to Algorithmic Design and Analysis

Introduction to Algorithmic Design and Analysis, available at $19.99, has an average rating of 4.5, with 32 lectures, 8 quizzes, based on 9 reviews, and has 1381 subscribers.

You will learn about Learn how to design & analyze algorithms. Become a better programmer. Enhance your problem solving and critical thinking skills. Learn to think like a computer scientist. This course is ideal for individuals who are Beginner developers or Math or computer science students It is particularly useful for Beginner developers or Math or computer science students.

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Summary

Title: Introduction to Algorithmic Design and Analysis

Price: $19.99

Average Rating: 4.5

Number of Lectures: 32

Number of Quizzes: 8

Number of Published Lectures: 32

Number of Published Quizzes: 8

Number of Curriculum Items: 40

Number of Published Curriculum Objects: 40

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn how to design & analyze algorithms.
Become a better programmer.
Enhance your problem solving and critical thinking skills.
Learn to think like a computer scientist.

Who Should Attend

Beginner developers
Math or computer science students

Target Audiences

Beginner developers
Math or computer science students

In this comprehensive course, students will embark on a journey to explore the fundamental principles and techniques of algorithmic design and analysis. With a strong focus on practical applications, this course is tailored to equip learners with the knowledge and skills required to solve complex computational problems efficiently.

Throughout the course, students will delve into various essential algorithmic concepts, including:

Divide and Conquer Algorithms: Learn to break down complex problems into simpler subproblems, solve them recursively, and combine the solutions to conquer the original problem.
Sorting: Gain insights into various sorting techniques, such as bubble sort, and mergesort, and how to analyze these algorithms.
Hash Maps: Understand the power of hashing and its role in creating efficient data structures for fast data retrieval and storage.
Stacks and Queues: Explore the functionality of these linear data structures and their applications in algorithmic design.
Linked Lists: Grasp the intricacies of linked lists and their role in dynamic memory allocation and data manipulation.
Dynamic Programming: Unravel the art of solving problems by breaking them down into overlapping subproblems and solving them in a bottom-up manner.
Graphs, BFS, and DFS: Delve into graph theory, and master breadth-first search (BFS) and depth-first search (DFS) algorithms to traverse and analyze complex networks.
Binary Search Trees: Learn about the structure and operations of binary search trees, and understand their importance in organizing hierarchical data efficiently.
Asymptotic Analysis: Develop a solid understanding of Big O, Big Omega, and Big Theta notations to evaluate the efficiency and scalability of algorithms.

By the end of this course, students will have a strong foundation in algorithmic design and analysis, empowering them to tackle complex computational challenges with confidence and precision.

Course Curriculum

Chapter 1: Math Fundamentals for Algorithms: Summations, Series, and Bounds

Lecture 1: Summations & Products

Lecture 2: Series & Sequences

Lecture 3: Bounding Summations

Chapter 2: Math Fundamentals for Algorithms: Set Theory

Lecture 1: Introduction to Sets

Lecture 2: De Morgan's Laws & Other Set Operations

Chapter 3: Math Fundamentals for Algorithms: Probability & Combinatorics

Lecture 1: Combinations & Permutations

Lecture 2: Intro to Probability

Lecture 3: Discrete Random Variables

Chapter 4: Math Fundamentals for Algorithms: Recursion

Lecture 1: Defining Recursive Functions

Lecture 2: Analyzing Recursive Functions

Chapter 5: Math Fundamentals for Algorithms: Asymptotic Analysis

Lecture 1: Asymptotic Analysis – Brief Overview

Lecture 2: Common Notations in Asymptotic Analysis

Lecture 3: Master Method

Chapter 6: Sorting and Divide-and-Conquer Algorithms

Lecture 1: Maximum Subarray Algorithm

Lecture 2: Maximum Subarray Pseudocode

Lecture 3: Analyzing Maximum Subarray Algorithm

Lecture 4: Bubble Sort

Lecture 5: Bubble Sort Pseudocode, Logic, and Analysis

Lecture 6: Merge Sort

Lecture 7: Merge Pseudocode

Chapter 7: Common Data Structures

Lecture 1: Linked Lists

Lecture 2: Linked List Operations & Pseudocode

Lecture 3: Stacks & Queues

Lecture 4: Stacks & Queues Pseudocode

Lecture 5: Hash Maps

Lecture 6: Hash Map Operations With Chaining

Lecture 7: Binary Search Trees

Lecture 8: Binary Search Tree Operations

Chapter 8: Dynamic Programming & Graph Algorithms

Lecture 1: Dynamic Programming

Lecture 2: Dynamic Programming Explanation & Rod-cutting Pseudocode

Lecture 3: Graphs, BFS, and DFS

Lecture 4: Graphs, BFS, and DFS Pseudocode

Instructors

Ingenium Academy
Where Modern Concepts Meet Real-World Value

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3 stars: 1 votes
4 stars: 4 votes
5 stars: 4 votes

Frequently Asked Questions

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