Algorithmic Design and Techniques from edX

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.

Module 2: Introduction In this module you will learn that programs based on efficient algorithms can solve the same problem billions of times faster than programs based on naïve algorithms. You will learn how to estimate the running time and memory of an algorithm without even implementing it. Armed with this knowledge, you will be able to compare various algorithms, select the most efficient ones, and finally implement them as our programming challenges!

Module 3: Greedy Algorithms In this module you will learn about seemingly naïve yet powerful class of algorithms called greedy algorithms. After you will learn the key idea behind the greedy algorithms, you may feel that they represent the algorithmic Swiss army knife that can be applied to solve nearly all programming challenges in this course. But be warned: with a few exceptions that we will cover, this intuitive idea rarely works in practice! For this reason, it is important to prove that a greedy algorithm always produces an optimal solution before using this algorithm. In the end of this module, we will test your intuition and taste for greedy algorithms by offering several programming challenges.

Module 4: Divide-and-Conquer In this module you will learn about a powerful algorithmic technique called Divide and Conquer. Based on this technique, you will see how to search huge databases millions of times faster than using naïve linear search. You will even learn that the standard way to multiply numbers (that you learned in the grade school) is far from the being the fastest! We will then apply the divide-and-conquer technique to design two efficient algorithms (merge sort and quick sort) for sorting huge lists, a problem that finds many applications in practice. Finally, we will show that these two algorithms are optimal, that is, no algorithm can sort faster!

Modules 5 and 6: Dynamic Programming In this final module of the course you will learn about the powerful algorithmic technique for solving many optimization problems called Dynamic Programming. It turned out that dynamic programming can solve many problems that evade all attempts to solve them using greedy or divide-and-conquer strategy. There are countless applications of dynamic programming in practice: from maximizing the advertisement revenue of a TV station, to search for similar Internet pages, to gene finding (the problem where biologists need to find the minimum number of mutations to transform one gene into another). You will learn how the same idea helps to automatically make spelling corrections and to show the differences between two versions of the same text.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Challenging algorithm design course

According to students, Algorithmic Design and Techniques is a challenging course. However, the programming assignments and testing are plentiful and rigorous. Students said that the lectures are generally clear and include useful slides. One student noted that the instructors have strong accents and sometimes gloss over important points.

Lectures are generally clear with useful slides.

"Lectures are mostly clear, well explained and provide useful downloadable slides."

Course offers ample programming assignments with rigorous testing.

"Plenty of programming assignments and rigorous testing of student submissions."

Course is challenging, especially if math background is weak.

"a lot more work than its authors claim"

"if you don't have a solid background in mathematics, you're likely to find those aspects of the syllabus poorly explained"

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

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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

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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.

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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

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

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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

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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.

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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.

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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.

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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