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Algorithmic Design and Techniques

Daniel Kane, Alexander S. Kulikov, Michael Levin, Pavel Pevzner, and Neil Rhodes

3.7

Based on 3 ratings

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Daniel Kane, Alexander S. Kulikov, Michael Levin, Pavel Pevzner, and Neil Rhodes

In this course, part of the Algorithms and Data Structures MicroMasters program, you will learn basic algorithmic techniques and ideas for computational problems, which arise in practical applications such as sorting and searching, divide and conquer, greedy algorithms and dynamic programming.

This course will cover theories, including:

how to sort data and how it helps for searching;
how to break a large problem into pieces and solve them recursively;
when it makes sense to proceed greedily;
how dynamic programming is used in genomic studies.

This course will cover theories, including:

how to sort data and how it helps for searching;
how to break a large problem into pieces and solve them recursively;
when it makes sense to proceed greedily;
how dynamic programming is used in genomic studies.

You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

What you'll learn

Essential algorithmic techniques - greedy algorithms, divide and conquer, binary search, sorting, dynamic programming
Best practices of implementing algorithms efficiently
Ways of testing and debugging programs

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What's inside

Learning objectives

Essential algorithmic techniques - greedy algorithms, divide and conquer, binary search, sorting, dynamic programming
Best practices of implementing algorithms efficiently

Ways of testing and debugging programs

Essential algorithmic techniques - greedy algorithms, divide and conquer, binary search, sorting, dynamic programming
Best practices of implementing algorithms efficiently
Ways of testing and debugging programs

Syllabus

Module 1: Welcome Here we will provide an overview of where algorithms and data structures are used (hint: everywhere) and walk you through a few sample programming challenges. The programming challenges represent an important (and often the most difficult!) part of this specialization because the only way to fully understand an algorithm is to implement it. Writing correct and efficient programs is hard; please don’t be surprised if they don’t work as you planned—our first programs did not work either! We will help you on your journey through the specialization by showing how to implement your first programming challenges. We will also introduce testing techniques that will help increase your chances of passing assignments on your first attempt. In case your program does not work as intended, we will show how to fix it, even if you don’t yet know which test your implementation is failing on.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces basic time and memory complexity concepts, which are foundational to computer science

Teaches algorithmic efficiency, which is highly relevant to software development and data analysis

Establishes a strong foundation in algorithms and data structures, making it suitable for beginners

Covers topics like greedy algorithms and dynamic programming, which are essential for solving optimization problems

Taught by Alexander S. Kulikov, Neil Rhodes, Michael Levin, Pavel Pevzner, and Daniel Kane, all renowned experts in algorithms

Highly relevant to fields like software development, data analysis, and bioinformatics

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Reviews summary

Foundational algorithms for computing

According to learners, this course offers a strong foundation in essential algorithmic techniques, including greedy algorithms, divide and conquer, and dynamic programming. Students praise the clear explanations and engaging lectures provided by the instructors. The programming challenges are frequently highlighted as a significant strength, offering practical application of the concepts learned and being particularly helpful for interview preparation. While some find the course challenging, especially the programming assignments and testing environment, the overall sentiment is highly positive, with many recommending it for anyone serious about understanding core algorithms.

Requires dedication; not for absolute beginners.

"This course requires significant time and effort; it's not a walk in the park."

"As someone with some programming background, I still found it quite demanding."

"It assumes some level of programming comfort coming in."

"Be prepared for a steep learning curve if you're new to algorithms."

Assignments require significant effort but are rewarding.

"The programming assignments are quite challenging but force you to deeply understand the material."

"I spent a lot of time on the coding problems, but they were worth it."

"Assignments are tough but provide necessary hands-on practice."

"Expect to work hard on the programming parts; they are not easy."

Helps prepare for technical interviews.

"This course is incredibly helpful for preparing for coding interviews."

"The problems are very similar to those asked in tech interviews."

"I feel much more confident tackling algorithmic problems after this course, great for interviews."

"Highly recommended for anyone prepping for a software engineering interview."

Instructors explain complex ideas clearly.

"Lectures are clear, concise, and well-paced, making complex topics understandable."

"The instructors do a fantastic job explaining difficult concepts simply."

"I found the video lectures very engaging and easy to follow."

"Explanations are top-notch, making learning algorithms enjoyable."

Provides solid basics in key algorithm types.

"The course material is really good, covering the essential algorithmic techniques."

"I got a solid foundation in greedy, divide-and-conquer, and dynamic programming."

"This course provides an excellent base for understanding common algorithms."

"It covers exactly what the syllabus promises for foundational algorithms."

Some issues reported with the grader/tests.

"The testing system can be frustrating at times; it's not always clear why a solution fails."

"I encountered some issues with the automated grader being overly strict."

"Debugging issues related to the testing environment was sometimes difficult."

"The test cases could be more informative when a submission fails."

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 Algorithmic Design and Techniques with these activities:

Algorithm Review

Show steps

Refresh prior knowledge of algorithms and data structures to enhance comprehension.

Browse courses on Algorithms

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Review basic concepts of algorithms (e.g., time complexity, recursion)
Recall common data structures (e.g., arrays, linked lists, trees)
Practice solving simple algorithmic problems
Participate in online discussions or forums to refresh understanding

Introduction to Algorithms by Thomas H. Cormen

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Gain a comprehensive understanding of algorithms by reviewing a classic textbook.

View Introduction to Algorithms, fourth edition on Amazon

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Acquire a copy of the book
Read the assigned chapters
Work through the practice exercises and problems
Summarize the key concepts and techniques covered in each chapter
Discuss the book with peers or a mentor to reinforce understanding

Algorithmic Study Notes

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Organize and consolidate course materials to enhance retention and understanding.

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Review lecture notes, textbook chapters, and other course materials
Create a structured outline or mind map of the key concepts
Summarize and condense the information into concise notes
Include examples, diagrams, and practice questions for reinforcement
Regularly review and update the notes

Six other activities

Expand to see all activities and additional details

Show all nine activities

Dive into Dynamic Programming with GeeksforGeeks

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Understand the concepts of dynamic programming through well-explained tutorials and examples.

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Visit the GeeksforGeeks Dynamic Programming page
Choose a tutorial or example that aligns with the course content
Read the tutorial or work through the example
Implement the discussed dynamic programming technique in your own code
Compare your implementation with the provided solutions

Peer Coding Sessions

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Engage in collaborative programming sessions with peers to reinforce understanding and provide support.

Show steps

Find a peer who shares similar interests and skill levels
Schedule regular coding sessions to work on problems together
Take turns explaining concepts, solving problems, and providing feedback
Share resources and discuss best practices
Evaluate progress and adjust the sessions based on needs

LeetCode Practice

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Solve algorithmic problems on LeetCode to improve critical thinking and problem-solving skills.

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Choose a LeetCode problem to solve
Analyze the problem statement and identify the key concepts
Design an efficient algorithm to solve the problem
Implement the algorithm in your preferred programming language
Submit your solution and review feedback

Algorithmic Blog

Show steps

Enhance understanding by creating a blog that explains algorithmic techniques and shares code examples.

Show steps

Choose a topic related to the course material
Research and gather information on the topic
Write a well-structured blog post that explains the concepts clearly
Include code examples and visualizations to illustrate the algorithms
Publish the blog post and share it with peers for feedback

Implement Sorting Algorithms

Show steps

Experiment with different sorting algorithms and practice implementing them efficiently.

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Choose a sorting algorithm to implement (e.g., bubble sort, selection sort, insertion sort)
Design the algorithm's steps in pseudocode
Implement the algorithm in your preferred programming language
Test your algorithm on different datasets and measure its time complexity
Compare the performance of your implementation with other popular sorting algorithms

Contribute to Open-Source Algorithm Projects

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Gain practical experience and deepen understanding by contributing to open-source algorithmic projects.

Show steps

Identify open-source algorithm projects on platforms like GitHub
Read the project documentation and choose an area to contribute to
Implement a feature or fix a bug based on the project's guidelines
Submit a pull request and collaborate with the project maintainers
Review the feedback and make necessary revisions to your contribution

Career center

Learners who complete Algorithmic Design and Techniques will develop knowledge and skills that may be useful to these careers:

Software Engineer

As a Software Engineer, you will be tasked with many of the same assignments you will practice in this course. Here you will learn algorithmic techniques that will help you write efficient and scalable code. This will give you a head start in designing and implementing software that will meet the needs of end users.

See salaries and explore the career path for Software Engineer

Data Scientist

In the field of Data Science, you will likely use many of the techniques taught in this course on a daily basis. It will help you to develop solutions to a wide range of problems that involve data analysis and machine learning.

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Machine Learning Engineer

As a Machine Learning Engineer, you will need to develop algorithms that can learn from data and make predictions. The techniques you will learn in this course will be essential for your success in this role.

See salaries and explore the career path for Machine Learning Engineer

Computer Scientist

This course may be helpful for Computer Scientists who want to learn more about algorithmic design and techniques. The concepts you will learn can be applied to a wide range of problems in computer science, including software development, data analysis, and machine learning.

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Quantitative Analyst

As a Quantitative Analyst, you will use mathematical and statistical methods to analyze data and make predictions. The techniques you will learn in this course will give you a strong foundation for success in this role.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

In the field of Operations Research, you will use mathematical and analytical methods to solve complex problems. The techniques you will learn in this course will help you to develop and implement solutions that can improve efficiency and productivity.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

As a Business Analyst, you will work with stakeholders to understand their needs and develop solutions that meet those needs. The techniques you will learn in this course will help you to gather and analyze data, and to develop and implement solutions that can improve business outcomes.

See salaries and explore the career path for Business Analyst

Data Analyst

In the field of Data Analytics, you will use data to identify trends and patterns, and to make predictions. The techniques you will learn in this course will give you a strong foundation for success in this role.

See salaries and explore the career path for Data Analyst

Statistician

As a Statistician, you will collect, analyze, and interpret data. The techniques you will learn in this course will give you a strong foundation for success in this role.

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Economist

As an Economist, you will use data to analyze economic trends and make predictions. The techniques you will learn in this course will give you a strong foundation for success in this role.

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Financial Analyst

As a Financial Analyst, you will use data to analyze financial trends and make predictions. The techniques you will learn in this course will give you a strong foundation for success in this role.

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Market Researcher

As a Market Researcher, you will use data to analyze market trends and make predictions. The techniques you will learn in this course will give you a strong foundation for success in this role.

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Survey Researcher

As a Survey Researcher, you will design and conduct surveys to collect data. The techniques you will learn in this course will give you a strong foundation for success in this role.

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User Experience Researcher

As a User Experience Researcher, you will use data to analyze how users interact with products and services. The techniques you will learn in this course will give you a strong foundation for success in this role.

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Interaction Designer

As an Interaction Designer, you will design and develop interactive products and services. The techniques you will learn in this course will give you a strong foundation for success in this role.

See salaries and explore the career path for Interaction Designer

Reading list

We've selected six 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 Algorithmic Design and Techniques.

Introduction to Algorithms, fourth edition

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Covers a wide range of algorithms, from basic data structures to advanced techniques, providing a comprehensive foundation in algorithm design and analysis.

Introduction to Algorithms, fourth edition

Hardcover

$$$$

Introduction to Algorithms, fourth edition

Kindle Edition

$$$