The Design and Analysis of Algorithm Masterclass from Udemy

The algorithm is used everywhere. People Don't know how Complex algorithms they are executing when doing there day to day tasks like Riding a Bi-Cycle, Travelling from one place to another even Watering Gardens.

If you are Coder then the Knowledge of algorithms is Very much important for you. The knowledge of Algorithm teach you How to Think to solve a Problem?

The algorithm is the concept that differentiates one average software engineer and one better software engineer. In our daily life in the industry, we used different kinds of algorithms to make the system faster, better, and more efficient.

But the problem is 90% of the freshers and graduates don’t have the basic knowledge of algorithms. That is the reason we make this Design and Analysis of algorithm Masterclass.

What you are Going to Learn?

Asymptotic Notations, Recursion, Divide and Conquer, Dynamic Programming, Dijkstra's, Bellman-Ford, Floyd Warshall Algorithm, Kruskal's Algorithm, Knapsack Problem, String Matching with Finite Automaton, Heap sort, Huffman Codes, n-Queens Algorithm, Rat in Maze, 0/1 Knapsack Problem, 15 Puzzle Problem, NP-Completeness, Approximation Algorithms

12 hours of HD content [Updated on 2022 December]
Assignment [Updated]
Study Note [Updated]
Certificate

Every Single Day we will check your questions and solve your queries.

Topics covered :

Enhance Your Logical Thinking Abilities
Learn various Popular Data Structures and their Algorithms.
Knowing Algorithm Well helps you to Solve the Problem in a Better Way.
Learn Big O, Big Omega Big Theta Notation
Linear Search, Recurrence Relations
Factorial, Tail Recursion
Towers of Hanoi
Merge Sort, Quick Sort, Heap Sort
Knapsack Problem, Minimum Spanning Tree: Kruskal's Algorithm, Minimum Spanning Tree: Prim's Algorithm
Huffman's Codes - Building Huffman Tree, Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd Warshall Algorithm
Brute Force Matcher
Pattern Pre-Processing
The Knuth Morris Pratt Algorithm
n-Queens Algorithm
Graph Coloring, Hamiltonian Cycles
0/1 Knapsack Problem
15 Puzzle Problem
NP-Completeness and Approximation Algorithms
Will get to know Real-time uses of all Algorithm
Get the answer of your "WHY" part behind the use of every Algorithm

What's inside

Learning objectives

Enhance your logical thinking abilities
Learn various popular data structures and their algorithms.
Knowing algorithm well helps you to solve the problem in a better way.
Learn big o, big omega big theta notation
Linear search, recurrence relations
Factorial, tail recursion
Towers of hanoi
Merge sort, quick sort, heap sort
Knapsack problem, minimum spanning tree: kruskal's algorithm, minimum spanning tree: prim's algorithm
Huffman's codes - building huffman tree, dijkstra's algorithm,bellman ford algorithm, floyd warshall algorithm
Brute force matcher

Pattern pre-processing
The knuth morris pratt algorithm
N-queens algorithm
Graph coloring, hamiltonian cycles
0/1 knapsack problem
15 puzzle problem
Np completeness and approximation algorithms
Will get to know real time uses of all algorithm
Get the answer of your "why" part behind use of every algorithm
Show more
Show less

Enhance your logical thinking abilities
Learn various popular data structures and their algorithms.
Knowing algorithm well helps you to solve the problem in a better way.
Learn big o, big omega big theta notation
Linear search, recurrence relations
Factorial, tail recursion
Towers of hanoi
Merge sort, quick sort, heap sort
Knapsack problem, minimum spanning tree: kruskal's algorithm, minimum spanning tree: prim's algorithm
Huffman's codes - building huffman tree, dijkstra's algorithm,bellman ford algorithm, floyd warshall algorithm
Brute force matcher
Pattern pre-processing
The knuth morris pratt algorithm
N-queens algorithm
Graph coloring, hamiltonian cycles
0/1 knapsack problem
15 puzzle problem
Np completeness and approximation algorithms
Will get to know real time uses of all algorithm
Get the answer of your "why" part behind use of every algorithm
Show more
Show less

Syllabus

Dynamic Programming

Fibonacci Numbers

Rod Cutting

Matrix Chain Multiplication

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers a wide range of fundamental algorithms, including sorting, searching, graph algorithms, and dynamic programming, which are essential for coding interviews and practical problem-solving

Explores NP-completeness and approximation algorithms, providing insights into the limitations of computation and strategies for dealing with intractable problems

Includes string matching algorithms like Knuth-Morris-Pratt, which are valuable for text processing and pattern recognition tasks

Features backtracking algorithms like n-Queens and Rat in Maze, which are useful for solving constraint satisfaction problems and exploring search spaces

Updated in December 2022, which suggests that the content is relatively current, but learners should still verify that the algorithms and approaches are up-to-date

Teaches the use of Big O, Big Omega, and Big Theta notations, which are critical for analyzing the efficiency and scalability of algorithms

Reviews summary

Comprehensive algorithm design and analysis

According to learners, this course offers a comprehensive dive into algorithm design and analysis, covering a wide range of topics from fundamentals like Big O notation and recursion to advanced areas like dynamic programming and graph algorithms. Many students found the explanations clear and well-structured, making complex concepts accessible. The course provides a solid theoretical foundation and includes assignments that help solidify understanding. While largely positive, some reviewers noted the course could benefit from more hands-on coding examples or deeper dives into practical applications.

Instructor is responsive to questions.

"The instructor answers questions quickly and clearly."

"Any queries I had were promptly addressed by the course creator."

"Support from the instructor on the discussion board was helpful."

"It's good to know the instructor is actively involved."

Good starting point for algorithm study.

"This course is a great starting point for anyone new to algorithms."

"If you are a beginner, this course provides a very good introduction."

"Even without much prior background, I was able to follow along."

"It builds concepts from the ground up, which is good for newcomers."

Assignments help practice learned concepts.

"The assignments are helpful for practicing and reinforcing the concepts taught."

"Doing the quizzes and assignments really helped solidify my understanding."

"The updated assignments provide good practice problems."

"I found the exercises relevant to the lecture content."

Provides strong theoretical background.

"The course gives a solid theoretical foundation for understanding how algorithms work."

"It explains the 'why' behind algorithms very well, which is crucial for analysis."

"This masterclass is excellent for building a strong theoretical understanding of DSA."

"I appreciate the focus on the mathematical and analytical aspects of algorithms."

Instructor explains complex topics clearly.

"The explanations for complex concepts are very clear and easy to follow."

"The instructor breaks down difficult algorithms into understandable steps."

"I found the lectures well-articulated and the pace was just right for me."

"Understanding Big O notation was much easier after these clear explanations."

Covers essential algorithms and data structures.

"It covers wide range of topics from basic analysis like big O to dynamic programming and graph algorithms."

"This course covers all the important algorithms that one needs to know for interviews and general understanding."

"I like the breadth of topics covered, from sorting to graph algorithms and NP-completeness."

"The course syllabus is comprehensive, covering fundamental to advanced algorithm techniques."

Could use more practical coding examples.

"Needs more hands-on coding examples. It's mostly theoretical."

"I wish there were more code implementations shown alongside the theory."

"The course is strong on theory but lacks practical coding exercises."

"Seeing more code walk-throughs would greatly enhance the learning experience."

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 Design and Analysis of Algorithm Masterclass with these activities:

Review Discrete Mathematics

Show steps

Strengthen your understanding of discrete mathematics concepts, which are fundamental to algorithm design and analysis.

Browse courses on Discrete Mathematics

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Review key concepts like sets, logic, and graph theory.
Work through practice problems to reinforce understanding.
Identify areas where you need further clarification.

Review 'Algorithms' by Robert Sedgewick and Kevin Wayne

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Gain a practical perspective on algorithm implementation with a book that emphasizes code examples.

View Introduction to Programming in Java: An... on Amazon

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Read chapters related to the course topics.
Study the Java code examples provided in the book.
Experiment with the code and modify it to understand the algorithms better.

Read 'Introduction to Algorithms' by Cormen et al.

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Supplement the course material with a detailed textbook that provides a strong theoretical foundation.

View Introduction to Algorithms, fourth edition on Amazon

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Read relevant chapters corresponding to the course modules.
Work through the examples and exercises in the book.
Compare the book's explanations with the course lectures.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Solve algorithm problems on LeetCode

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Practice implementing algorithms to improve coding skills and problem-solving abilities.

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Choose problems related to the topics covered in the course.
Implement the algorithms in your preferred programming language.
Test your solutions and debug any errors.
Analyze the time and space complexity of your solutions.

Create a blog post explaining Dynamic Programming

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Solidify your understanding of dynamic programming by explaining the concept in a clear and concise manner.

Show steps

Research and gather information on dynamic programming.
Outline the key concepts and examples to include.
Write the blog post, focusing on clarity and accuracy.
Edit and proofread the post before publishing.

Implement a pathfinding visualizer

Show steps

Apply your knowledge of pathfinding algorithms by creating a visual representation of their execution.

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Choose a pathfinding algorithm (e.g., Dijkstra's, A*).
Design a graphical interface to represent the search space.
Implement the algorithm and visualize its steps.
Add features to compare different algorithms.

Answer questions on algorithm forums

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Reinforce your understanding by helping others learn and debug their code.

Show steps

Find online forums or communities related to algorithms.
Browse the questions and identify those you can answer.
Provide clear and helpful explanations to the questions.
Offer code examples or debugging tips when appropriate.

Career center

Learners who complete The Design and Analysis of Algorithm Masterclass will develop knowledge and skills that may be useful to these careers:

Algorithm Developer

An algorithm developer specializes in the creation and refinement of algorithms. This course, titled 'The Design and Analysis of Algorithm Masterclass,' is directly relevant to this role, offering a comprehensive overview of essential algorithms and techniques. An aspiring algorithm developer will benefit from the course's exploration of dynamic programming, graph algorithms, and string matching, all of which are common tasks. This course also covers recursion and divide and conquer techniques, which are fundamental for algorithm design. The course can lead to an understanding of not just how algorithms function, but also why they work, as the course aims to answer the “WHY” behind each algorithm.

See salaries and explore the career path for Algorithm Developer

Software Engineer

A software engineer relies on a deep understanding of algorithms to create efficient and effective software solutions. This course, with its focus on algorithm design and analysis, provides a crucial foundation for any aspiring software engineer. The course covers various popular algorithms and data structures, which are essential for building robust applications. The ability to analyze algorithmic complexity, a skill taught in this course, enables a software engineer to optimize code for performance, using tools like Big O notation. This course is particularly useful for developing problem-solving skills, a hallmark of a successful software engineer.

See salaries and explore the career path for Software Engineer

Data Scientist

A data scientist uses algorithms to analyze data, build models, and extract insights. This course helps a data scientist build a helpful foundation, focusing on the understanding and application of algorithms like graph algorithms and string matching, among others. The course covers core concepts such as asymptotic notation and algorithmic complexity, which are essential for optimizing data processing tasks. A data scientist will benefit from the included discussions on dynamic programming and greedy algorithms which can apply to machine learning and data analysis. This course may be useful for data scientists who want to deepen their understanding of the algorithmic foundations of data analysis.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A machine learning engineer builds and implements machine learning models, often relying on sophisticated algorithms. This course may be useful for a machine learning engineer, covering topics like dynamic programming, shortest path, and string matching algorithms. The course also covers NP-completeness and approximation algorithms, which are concepts that can help a machine learning engineer understand the bounds of computational problems. The course is especially helpful for those looking to understand various algorithms from the ground up. The course's focus on algorithmic thinking helps one design better performing models.

See salaries and explore the career path for Machine Learning Engineer

Bioinformatician

A bioinformatician uses algorithms and software tools to analyze biological data. This course, which explores string matching algorithms and other computational methods, helps a bioinformatician build a useful foundation. The course also provides an overview of dynamic programming, which is used in the study of biological sequences. A bioinformatician would find the course's focus on graph algorithms and the included discussion on approximation algorithms useful. This course may be especially useful for those interested in working with genome sequencing, or other biological data.

See salaries and explore the career path for Bioinformatician

Robotics Engineer

A robotics engineer designs, builds, and programs robots, often relying on complex algorithms for navigation and control. This course may be useful, as it covers algorithms that are important for robotics, like pathfinding algorithms including Dijkstra's and Bellman-Ford. The course's focus on data structure and algorithmic thinking helps one design optimal robotic systems. The course includes coverage of dynamic programming and recursion, both of which are useful problem solving tools for robotics. The course can help a robotics engineer understand the role algorithms play in robotic systems.

See salaries and explore the career path for Robotics Engineer

Game Developer

A game developer uses algorithms and data structures to create engaging and performant game experiences. This course may be useful for an aspiring game developer, as it covers a broad range of algorithms useful for game development, such as pathfinding algorithms, like Dijkstra's and Floyd Warshall, and algorithms useful for game mechanics design. The course's focus on problem-solving will help game developers think algorithmically. The course covers algorithmic complexity, which is essential to ensure games run smoothly. The course is particularly useful for developing a deep understanding of the algorithmic underpinnings of game development.

See salaries and explore the career path for Game Developer

Computational Biologist

A computational biologist uses algorithms and computational techniques to analyze biological data. This course is useful for a computational biologist, as it includes many algorithms that can be applied to biological data analysis, such as string matching algorithms and graph algorithms. This course helps build a foundation with the included discussion on dynamic programming which can be applied to sequence alignment. The course will be useful for those looking to understand how to think algorithmically. The course's focus on problem solving helps one analyze complex biological problems more effectively.

See salaries and explore the career path for Computational Biologist

Quantitative Analyst

A quantitative analyst uses mathematical and statistical models to analyze financial markets and manage risk, and relies on an understanding of algorithms. This course may be useful for a quantitative analyst, as it provides an understanding of how algorithms solve problems. The course's inclusion of optimization algorithms is important for a quantitative analyst. This course may help a quantitative analyst explore the algorithmic basis of financial models. The course covers foundational concepts for analyzing complex problems and thinking algorithmically, both useful for quantitative finance.

See salaries and explore the career path for Quantitative Analyst

Systems Analyst

A systems analyst evaluates and designs systems, and needs an understanding of algorithmic efficiency. This course may be useful for a systems analyst, as it introduces the various algorithms that are the foundation for many systems. This course also focuses on algorithmic complexity using concepts like Big O notations, which help a systems analyst design robust and performant systems. The course's focus on problem solving will help a systems analyst analyze complex problems. This course may be helpful for those who want to better understand the algorithmic architecture of systems.

See salaries and explore the career path for Systems Analyst

Data Analyst

A data analyst interprets data and presents findings, and benefits from an understanding of algorithms. This course may be helpful for a data analyst, as it explores various algorithms and data structures, concepts that are helpful for understanding how data is processed. The course can help a data analyst develop a foundational understanding of how algorithms can be used to solve various problems related to data transformation. The course's focus on problem solving will be useful too. This course may be especially useful for those data analysts who wish to understand the computational basis of their work.

See salaries and explore the career path for Data Analyst

Database Administrator

A database administrator manages and maintains databases using algorithms for data retrieval and other operations. This course may helpful for a database administrator since it covers a wide variety of algorithms. The course's focus on complexity and scalability will help a database administrator design efficient and performant databases. The included algorithm analysis helps a database administrator understand the efficiency of different database operations. The course's focus on problem solving is useful for any database admin.

See salaries and explore the career path for Database Administrator

Cryptography Engineer

A cryptography engineer designs and implements cryptographic systems, often relying on complex algorithms. This course may be helpful, as it offers a strong foundation in algorithmic principles. The course's focus on various algorithms, including string matching and graph algorithms, is useful for understanding the fundamentals of cryptographic design. The course also covers complexity analysis and NP-completeness, useful concepts for any cryptography engineer. The algorithmic thinking promoted by this course is valuable for designing secure and robust cryptographic systems.

See salaries and explore the career path for Cryptography Engineer

Financial Software Developer

A financial software developer creates software solutions for the financial industry and needs an understanding of various algorithms. This course may be useful for a financial software developer because it provides a foundation in essential algorithms and data structures. The course's focus on algorithmic efficiency can help a financial software developer design performant systems for use in high frequency financial applications. The course's coverage of dynamic programming and greedy algorithms can be useful. The focus on problem solving helps one design robust financial systems.

See salaries and explore the career path for Financial Software Developer

Operations Research Analyst

An operations research analyst uses mathematical and analytical methods to optimize organizational operations, which involves a deep understanding of algorithms. This course may be helpful because it introduces various algorithms and techniques, such as dynamic programming and shortest path, which are fundamental for problem solving in operations research. This course may be helpful for those looking to understand how algorithms can be applied to complex real world problems. The course's emphasis on algorithmic thinking will be beneficial.

See salaries and explore the career path for Operations Research Analyst

The Design and Analysis of Algorithm Masterclass

What's inside

Learning objectives

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Reviews summary

Comprehensive algorithm design and analysis

Activities

Career center

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