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Lucas Bazilio

You’ve just stumbled upon the most complete, in-depth Algorithms and Data Structures course online.

Whether you want to:

- build the skills you need to get your first programming job

- move to a more senior software developer position

- become a computer scientist mastering in computation

- or just learn algorithms and data structures to be able to create your own apps quickly.

...this complete Algorithms and Data Structures Masterclass is the course you need to do all of this, and more.

Read more

You’ve just stumbled upon the most complete, in-depth Algorithms and Data Structures course online.

Whether you want to:

- build the skills you need to get your first programming job

- move to a more senior software developer position

- become a computer scientist mastering in computation

- or just learn algorithms and data structures to be able to create your own apps quickly.

...this complete Algorithms and Data Structures Masterclass is the course you need to do all of this, and more.

This course is designed to give you the Algorithms and Data Structures skills you need to become an advanced developer. By the end of the course, you will understand Algorithms and Data Structures extremely well and be able to build your own apps and be productive as a computer scientist and software developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete Algorithms and Data Structures course. It’s designed with simplicity and seamless progression in mind through its content.This course assumes no previous coding experience and takes you from absolute beginner core concepts. You will learn the core computer science skills and master advanced algorithms. It's a one-stop shop to be an expert in computer science. If you want to go beyond the core content you can do so at any time.

Here’s just some of what you’ll learn

(It’s okay if you don’t understand all this yet, you will in the course)

  • Big O Notation, Big Omega, Big Theta

  • Complexity Analysis of Algorithms

  • Divide and Conquer

  • Dictionaries (Maps)

  • Stacks and Queues

  • Binary Search Trees

  • AVL Trees

  • Priority Queues

  • Graphs

  • Depth-First Search and Breadth-First Search

  • Generation and Exhaustive Search (Backtracking)

  • Greedy Algorithms

  • Dynamic Programming

  • Flow Networks

  • Ford-Fulkerson Algorithm

  • Hash Tables

  • Boom Filters

  • Max-flow Min-cut Algorithm

  • Fibonacci Heaps

  • How to develop powerful applications using Advanced Data Structures and Algorithms.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

There’s no risk either.

This course comes with a full 30-day money-back guarantee. Meaning if you are not completely satisfied with the course or your progress, simply let me know and I’ll refund you 100%, every last penny no questions asked.

You either end up with Algorithms and Data Structures skills, go on to develop great programs and potentially make an awesome career for yourself, or you try the course and simply get all your money back if you don’t like it…

You literally can’t lose.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Algorithms and Data Structures brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, Algorithms and Data Structures are waiting. )

Enroll now

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What's inside

Learning objectives

  • Become an advanced, confident, and modern developer from scratch
  • Become job-ready by understanding how algorithms and data structures really work behind the scenes
  • Learn, implement and use different data structures
  • Get fast and friendly support in the q&a area
  • How to think and work like a developer: problem-solving, researching, workflows
  • Complex concepts like the dynamic programming, backtracking, flow networks, etc.
  • Practice your skills with 20+ challenges and assignments (solutions included)

Syllabus

Know the topics that will be covered through the course.

On this lesson we introduce the course and mention some of the main topics that we will work along the course.

Read more

On this lesson we study the course structure.

On this lesson we introduce some of the topics of Asymptotic Notation that we will study.

In this lesson we introduce the time complexity of an algorithm.

In this lesson we study the Big O Notation and see some examples.

In this lesson we study the Big Ω Notation and see some examples.

In this lesson we study the Big Θ Notation and see some examples.

On this lesson we solve the exercise 1 on cost analysis.
In this exercise we need to give the cost of different fragments of code.

On this lesson we solve the exercise 2 on cost analysis.
In this exercise we need to give the cost of different fragments of code.

On this lesson we introduce the dictionary data structure.

On this lesson we introduce how we can implement dictionaries in C++.

In this lesson we introduce the iteration over a dictionary.

On this lesson we see some more ways to iterate over a dictionary.

On this lesson we study the fundamental methods of dictionaries used in C++.

In this lesson we solve problem 1 of the problems section on dictionaries.

In this lesson we solve problem 2 of the problems section on dictionaries.

On this lesson we solve problem 3 of the problems section on dictionaries.

On this lesson we introduce the Stack data structure.

On this lesson we learn how to use Stacks in C++.

On this lesson we solve problem 1 of the problems section on Stacks.

On this lesson we solve problem 2 of the problems section on Stacks.

On this lesson we solve problem 3 of the problems section on Stacks.

On this lesson we introduce the Queue data structure.

On this lecture we see how to work with queues.

On this lesson we solve problem 1 of the problems section on Queues.

On this lesson we solve problem 2 of the problems section on Queues.

On this lesson we introduce the vector data structure.

On this lesson we begin to work with vectors.

On this lesson we study the fundamental methods of the vectors C++ library.

On this lesson we solve problem 1 of the problems section on Vectors.

On this lesson we solve problem 2 of the problems section on Vectors.

On this lesson we solve problem 3 of the problems section on Vectors.

On this lesson we introduce the Basic Sorting Algorithms section, and study the idea of sorting.

On this lesson we study the selection sort algorithm.

On this lesson we study the insertion sort algorithm.

On this lesson we study the bubble sort algorithm.

On this lesson we study the merge sort algorithm.

On this lecture we make a brief introduction to sets in computer science.

On this lecture we see how to work with sets.

On this lesson we introduce Graphs.

On this lesson we study the two main representations of graphs.

The adjacency list and the adjacency matrix.

On this lesson we study the adjacency matrix implementation for representing graphs. Implemented in C++.

On this lesson we study the adjacency list implementation for representing graphs. Implemented in C++.

On this lecture we introduce the Depth First Search Algorithm.

On this lecture we implement the Depth First Search Algorithm.
This algorithm is recursive. On future lessons we will study the iterative algorithm.

On this lesson we implement the General Depth First Search Algorithm to traverse all nodes in the Graph, even if the graph is disconnected.

On this lesson we solve problem 1 of the problems section on Depth First Search.

On this lesson we solve problem 2 of the problems section on Depth First Search.

On this lesson we solve problem 3 of the problems section on Depth First Search.

On this lesson we introduce the Breadth First Search Algorithm.

On this lecture we implement the Breath First Search Algorithm.

On this lesson we solve problem 1 of the problems section on Breadth First Search.

On this lesson we solve problem 2 of the problems section on Breadth First Search.

On this lecture we introduce the Topological Sort algorithm.

On this lecture we explain the Topological Sort algorithm.

On this lesson we implement the topological sort algorithm.

On this lesson we solve problem 1 of the problems section on Topological Sort.

On this lecture we introduce Binary Trees.

On this lecture we learn how to implement the binary tree data structure.

On this lecture we solve problem 1 of the problems section on Binary Trees.

On this lecture we solve problem 2 of the problems section on Binary Trees.

On this lecture we solve problem 3 of the problems section on Binary Trees.

On this lecture we solve problem 4 of the problems section on Binary Trees.

On this lecture we solve problem 5 of the problems section on Binary Trees.

On this lecture we solve problem 6 of the problems section on Binary Trees.

On this lecture we solve problem 7 of the problems section on Binary Trees.

On this lesson we introduce the flow network concept.

We introduce the statements of two flow network problems: max flow and min cut.

In this lesson we explain the foundation of residual graph in flow networks.

On this lecture we study the concept of augmenting path in Flow Networks.

On this lesson we introduce brute force algorithms.

On this lesson we introduce the backtracking technique.

On this lesson we provide a generic algorithm for solving backtracking problems.

On this lesson we solve problem 1 of the problems section on Backtracking.

On this lesson we solve problem 2 of the problems section on Backtracking.

On this lesson we solve problem 3 of the problems section on Backtracking.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Covers Big O Notation, which is fundamental for analyzing algorithm efficiency and is a core concept in computer science curricula
Includes practice problems with solutions, which allows learners to reinforce their understanding and build practical problem-solving skills
Explores advanced topics like Dynamic Programming and Flow Networks, which are essential for tackling complex computational problems
Uses C++ for implementation examples, which is a widely used language in systems programming and performance-critical applications
Teaches data structures such as Stacks, Queues, and Vectors, which are implemented using the C++ library
Focuses on fundamental methods of dictionaries used in C++, which may require learners to have some familiarity with the language

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Reviews summary

Comprehensive algorithms and data structures course

According to learners, this course offers comprehensive coverage of algorithms and data structures, living up to its 'complete' title by addressing everything from Big O notation to advanced topics like Dynamic Programming. Many appreciate the focus on practical problem-solving, finding that the exercises significantly reinforce concepts. However, while the course aims for beginners, the use of C++ for implementations means some prior coding experience is beneficial, and true beginners might find the pace challenging in later sections. Despite this, the course provides a solid foundation for those looking to build skills for a career in software development.
Course uses C++ for coding examples
"Be aware that all the implementations are in C++. You need at least some familiarity with the language."
"The course uses C++ throughout for coding examples and exercises."
"While C++ is used, the concepts are explained well enough to be transferable to other languages if you focus on the logic."
Generally explains concepts clearly
"The explanations for Big O and basic data structures like trees and graphs were very clear."
"The instructor does a good job explaining complex ideas in an understandable way for the most part."
"Some lectures were a bit harder to follow than others, but overall, the clarity was good."
Exercises reinforce concepts effectively
"The problems section was very helpful. Working through them really cemented my understanding."
"I loved the hands-on coding problems; they helped me apply what I learned in the lectures."
"Solving the exercises provided was crucial for truly grasping the algorithms."
Covers a wide range of AD&S topics
"This course covers almost every topic you could want when learning algorithms and data structures."
"It starts from the basics like Big O notation but goes deep into complex algorithms."
"I feel this really is a complete course on algorithms and data structures, just as the title says."
May require prior coding experience
"Despite the description saying no prior coding experience is needed, I think you should have some basic C++ knowledge."
"As a complete beginner, I found the pace very fast, especially once we moved beyond the initial concepts."
"This course is most helpful if you already have basic programming skills, particularly in C++."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in The Complete Algorithms and Data Structures Course with these activities:
Review Big O Notation
Refresh your understanding of Big O notation to better grasp algorithm efficiency concepts covered in the course.
Browse courses on Big O Notation
Show steps
  • Review the formal definition of Big O notation.
  • Practice determining the Big O complexity of simple code snippets.
  • Work through examples of common algorithm complexities (e.g., O(n), O(log n), O(n^2)).
Review: Introduction to Algorithms
Deepen your understanding of algorithms with a comprehensive textbook.
Show steps
  • Read the chapters relevant to the course topics.
  • Work through the examples and exercises.
  • Compare the book's explanations with the course material.
Implement Data Structures from Scratch
Reinforce your understanding of data structures by implementing them without using built-in libraries.
Show steps
  • Choose a data structure (e.g., linked list, stack, queue, binary tree).
  • Define the basic operations for the data structure (e.g., insert, delete, search).
  • Write code to implement these operations.
  • Test your implementation thoroughly.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve LeetCode Problems on Trees
Improve your problem-solving skills by tackling tree-related coding challenges on platforms like LeetCode.
Show steps
  • Select a set of LeetCode problems focusing on tree data structures.
  • Attempt to solve each problem independently.
  • Analyze the provided solutions and optimize your code.
  • Reflect on the problem-solving strategies used.
Build a Pathfinding Visualizer
Apply your knowledge of graph algorithms by creating a visual representation of pathfinding algorithms.
Show steps
  • Choose a pathfinding algorithm (e.g., Dijkstra's, A*).
  • Design a visual interface to represent the graph and the algorithm's progress.
  • Implement the pathfinding algorithm.
  • Integrate the algorithm with the visual interface.
  • Add features to compare different algorithms.
Create a Blog Post on Dynamic Programming
Solidify your understanding of dynamic programming by explaining the concept in a blog post.
Show steps
  • Research and gather information on dynamic programming.
  • Outline the key concepts and examples.
  • Write a clear and concise explanation of dynamic programming.
  • Include code examples and illustrations.
  • Publish the blog post and share it with others.
Review: Cracking the Coding Interview
Prepare for technical interviews by studying common algorithm and data structure questions.
Show steps
  • Read the chapters on relevant data structures and algorithms.
  • Practice solving the interview questions.
  • Analyze the provided solutions and explanations.

Career center

Learners who complete The Complete Algorithms and Data Structures Course will develop knowledge and skills that may be useful to these careers:
Algorithm Developer
An Algorithm Developer designs and implements algorithms for various applications. This course is directly relevant, as it provides in-depth knowledge of algorithms and data structures. The course helps Algorithm Developers by covering topics like complexity analysis, sorting algorithms, and graph algorithms, enabling them to design and implement efficient and optimized algorithms. The focus on problem-solving and practical implementation makes this course an ideal choice for aspiring Algorithm Developers. The course is particularly useful due to its coverage of advanced algorithms and data structures.
Software Engineer
A Software Engineer designs, develops, and tests software applications. This course helps provide a comprehensive understanding of algorithms and data structures, which are essential for efficient and effective software development. By mastering topics like Big O notation and various data structures such as stacks, queues, and trees, learners are better prepared to write optimized code and solve complex problems. The course's focus on practical implementation and problem-solving directly translates to the day-to-day tasks of a Software Engineer. The course may be particularly helpful due to its coverage of advanced algorithms and data structures.
Technical Lead
A Technical Lead is responsible for guiding a team of developers and making technical decisions. This course helps them by providing a deep understanding of algorithms and data structures, which is essential for making informed decisions about software design and implementation. Topics like complexity analysis, and various data structures are crucial for leading a team effectively. The course may be particularly useful due to its coverage of advanced algorithms and data structures.
Data Scientist
A Data Scientist analyzes large datasets to extract meaningful insights and build predictive models. This course helps them by providing a strong foundation in algorithms and data structures, which are crucial for efficient data processing and analysis. The course covers topics like complexity analysis, sorting algorithms, and graph theory, all of which are relevant to data science tasks. With this course, Data Scientists can optimize their data processing pipelines and develop more efficient algorithms for machine learning. The course may be particularly useful due to its coverage of advanced algorithms and data structures.
Software Architect
A Software Architect designs the overall structure of software systems. This course helps provide a deep understanding of algorithms and data structures, which is essential for making informed decisions about system design. The course covers a wide range of topics, from basic data structures to advanced algorithms, giving a Software Architect the knowledge needed to optimize system performance and scalability. The course may be particularly useful due to its coverage of advanced algorithms and data structures.
Research Scientist
A Research Scientist conducts research in computer science or related fields. This course helps provide a strong foundation in algorithms and data structures, which is essential for conducting research in many areas of computer science. The course covers a wide range of topics, including advanced algorithms and data structures, giving a Research Scientist the knowledge needed to develop new algorithms and solve complex problems. An advanced degree is typically required. The course is particularly useful due to its coverage of advanced algorithms and data structures.
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course helps provide the algorithmic foundation necessary for implementing and optimizing machine learning algorithms. Topics like dynamic programming, graph algorithms, and data structures covered in the course are directly applicable to building efficient and scalable machine learning systems. A strong understanding of these concepts allows a Machine Learning Engineer to make informed decisions about model selection and optimization. The course may be particularly useful due to its coverage of advanced algorithms and data structures.
Full-Stack Developer
A Full Stack Developer works on both front-end and back-end development. This course helps them by providing a strong foundation in algorithms and data structures, which are crucial for building efficient and scalable web applications. Topics like data structures, sorting algorithms, and graph algorithms covered in the course are relevant to both front-end and back-end development. The course's broad coverage of computer science concepts makes it a valuable asset for any Full Stack Developer. The course may be particularly useful due to its coverage of advanced algorithms and data structures.
Computer Science Professor
A Computer Science Professor teaches computer science concepts and conducts research. This course helps provide a comprehensive understanding of algorithms and data structures, which is essential for teaching and research in computer science. The course covers a wide range of topics, from basic data structures to advanced algorithms, providing a solid foundation for explaining these concepts to students. Furthermore, the course's emphasis on problem-solving and practical implementation can inform research projects. An advanced degree is typically required. The course is particularly useful due to its coverage of advanced algorithms and data structures.
Data Engineer
A Data Engineer builds and maintains the infrastructure for data storage and processing. This course helps provide valuable knowledge of data structures and algorithms, which are essential for designing efficient data pipelines. Understanding topics like hash tables, trees, and sorting algorithms, covered in the course can help Data Engineers optimize data storage and retrieval processes. The course may be particularly useful due to its emphasis on practical implementation and problem-solving.
Game Developer
A Game Developer designs and develops video games. This course helps them by providing the necessary skills in algorithms and data structures to optimize game performance. Topics such as graph algorithms, searching algorithms, and data structures are crucial for efficient game development. The ability to implement and optimize algorithms is essential for creating high-performance games. The course's coverage of graphs may be particularly helpful.
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. This course may be useful by providing a deeper understanding of data structures, which are fundamental to database design and optimization. Topics such as hash tables, trees, and indexing techniques covered in the course can help Database Administrators optimize database performance and ensure data integrity. Knowledge of algorithm analysis can also help in troubleshooting performance bottlenecks. The course's coverage of dictionaries may be particularly helpful.
Security Engineer
A Security Engineer focuses on protecting computer systems and networks from security threats. This course may be useful by offering knowledge of algorithms and data structures that are relevant to security engineering. Topics like hash tables, bloom filters, and graph algorithms covered in the course can be applied to security-related tasks such as intrusion detection and vulnerability analysis. The course's coverage of hash tables and bloom filters may be particularly helpful.
Quantitative Analyst
A Quantitative Analyst develops and implements mathematical models for financial analysis. This course may be useful by providing a solid understanding of algorithms and data structures, which are essential for efficient data processing and analysis in finance. Topics like complexity analysis, sorting algorithms, and graph algorithms, covered in the course can be applied to financial modeling and risk management. The course's emphasis on problem-solving and practical implementation makes it a valuable asset for any Quantitative Analyst.
Embedded Systems Engineer
An Embedded Systems Engineer designs software for embedded systems. This course may be useful by providing valuable knowledge of algorithms and data structures, which are essential for optimizing code for resource-constrained environments. Topics like data structures, sorting algorithms, and graph algorithms covered in the course are relevant to embedded systems development. The course's emphasis on practical implementation and problem-solving makes it a valuable asset for any Embedded Systems Engineer.

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in The Complete Algorithms and Data Structures Course.
Comprehensive guide to preparing for coding interviews, with a strong focus on algorithms and data structures. It includes a wide range of practice problems and detailed solutions. It valuable resource for anyone looking to improve their problem-solving skills and prepare for technical interviews. It provides practical application of the concepts learned in the course.
Comprehensive textbook covering a wide range of algorithms in depth. It is often used as a primary textbook in university-level algorithms courses. It provides rigorous analysis and clear explanations, making it a valuable resource for understanding the theoretical underpinnings of algorithms and data structures. It adds significant depth to the topics covered in the course.

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