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Master the art of Dynamic Programming

4.3 Filled star Filled star Filled star Filled star Empty star
Based on 690 ratings
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Ajay Prakash

Implementing dynamic programming algorithms is more of an art than just a programming technique. Dynamic programming problems are also very commonly asked in coding interviews but if you ask anyone who is preparing for coding interviews which are the toughest problems asked in interviews most likely the answer is going to be dynamic programming.

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Implementing dynamic programming algorithms is more of an art than just a programming technique. Dynamic programming problems are also very commonly asked in coding interviews but if you ask anyone who is preparing for coding interviews which are the toughest problems asked in interviews most likely the answer is going to be dynamic programming.

In fact, dynamic programming problems are very easy to solve once you understand the theory in depth and know certain tricks. Most of the dynamic programming problems share some common elements and if you know how to identify those things you can come up with solutions easily.

In this course, you will learn

1. The in-depth theory behind dynamic programming

2. Recursion and backtracking techniques

3. A step by step approach to come up with dynamic programming solutions to a given problem from scratch

4. Applying step by step approach for one-dimensional dynamic programming problems with detailed examples

5. Applying step by step approach for multi dimensional dynamic programming problems with detailed examples

6. How to analyze the time and space complexities of recursive solutions as well as dynamic programming solutions

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What's inside

Learning objectives

  • Dynamic programming
  • Step by step approach to solve any dynamic programming problem.
  • Detailed solutions with code in java and python to over 15 popular dynamic programming questions.
  • Recursion
  • Backtracking
  • Algorithms
  • Data structures

Syllabus

Introduction to this course
Introduction
Understand recursion in depth and why recursion is important for Dynamic Programming.

Introduction to recursion and why it is important for dynamic programming.

Read more

Exercise questions to practice recursion.

Solutions to the exercise problems.

An introduction to backtracking , which is a very useful programming technique to solve many algorithm problems.

Backtracking helps us implement naive recursive solutions from scratch which can later be optimized using Dynamic programming techniques.

Exercise problems to practice backtracking.

An introduction to Dynamic programming, different approaches to implement dynamic programming solutions and pros and cons of each of those approaches.

Exercise problem to practice top down and bottom up approach for implementing Dynamic programming solution.

Introduction to two dimensional dynamic programming problems and a detailed explanation of how the step by step approach can be used to solve such problems with an example.

Example used : Minimum cost patch to travel from top left corner in a grid to bottom right corner of the same grid.

An introduction to optimization problems, optimal substructure property and recursive optimization procedure.

A detailed explanation of step by step approach to solve any dynamic programming solution from scratch with knapsack problem as the example.

A detailed explanation of how to reconstruct the actual solution to Dynamic programming problems with knapsack problem as the example.

Exercise to better understand the step by step approach by applying the approach to solve a nice dynamic programming problem.

Introduction to identifying and solving one dimensional dynamic programming problems with an example. The example problem is optimum way to rob a row of houses without being caught by the police.

A detailed explanation about using step by step approach to solve the rod cutting problem from scratch.

O(nlgn) solution for "Longest Increasing Subsequence"

https://www.geeksforgeeks.org/longest-monotonically-increasing-subsequence-size-n-log-n/

Exercise problems to practice step by step approach for solving one dimensional dynamic programming problems.

1. Minimum cost of reaching the last stone by jumping a maximum of X stones at a time

2. Counting the number of ways in which a string can be broken completely into valid words.

A detailed explanation of how we can use step by step approach to solve one of the classic two dimensional dynamic programming problem.

Longest common subsequence.

A detailed explanation of using step by step approach to solve the "minimum number of deletions required to make a string palindrome" problem.

A detailed explanation of solving "Edit distance" problem using step by step approach.

A detailed explanation of solving "Regular expression matching" problem using step by step approach and also analyzing its time and space complexity.

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Offers a step-by-step approach to dynamic programming, which can be helpful for coding interviews
Provides detailed solutions with code in both Java and Python, which are widely used in software development
Explores recursion and backtracking techniques, which are fundamental concepts in computer science curricula
Examines time and space complexities of recursive and dynamic programming solutions, which is crucial for algorithm optimization
Focuses on dynamic programming problems, which are frequently encountered in software engineering and algorithm design
Requires learners to understand recursion in depth, which may require additional study for some learners

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Reviews summary

Deep dive into dynamic programming

According to learners, this course offers a solid foundation (positive) and a highly effective step-by-step approach (positive) for tackling dynamic programming problems, especially for those preparing for coding interviews. Many appreciate the clear explanations (positive) and the inclusion of numerous detailed examples (positive) with code in Java and Python. While the course is generally well-structured (positive), some students felt the difficulty progression could be improved and wished for more challenging problems or advanced topics (warning) to truly achieve 'mastery'. Overall, it is seen as a great starting point (positive) for understanding DP concepts.
Best for those with some programming background.
"While the course is good, I think a solid understanding of recursion is a prerequisite to fully benefit from it."
"Learners should have a basic grasp of data structures and algorithms before taking this course."
"It's not for absolute beginners in programming; some prior algorithm knowledge helps a lot."
"Comes easier if you already have a decent understanding of recursion and basic algorithms."
Includes code in Java and Python.
"It was great to have code examples provided in both Java and Python."
"The inclusion of solutions in both popular interview languages was very convenient."
"Having code in both Java and Python made it easier to follow along regardless of my preferred language."
"Liked the side-by-side Java and Python code for problems."
Concepts are well-explained with helpful illustrations.
"The explanations were very clear and the examples used to illustrate dynamic programming concepts were well-chosen and easy to follow."
"Instructor explains concepts very clearly and breaks down complex ideas into understandable parts."
"I appreciate how detailed the examples are, especially the walkthroughs of classic problems like Knapsack and LCS."
"The lectures provide a great level of detail without being overwhelming."
Directly applicable to coding interview questions.
"This course is highly relevant for anyone preparing for coding interviews. The problems covered are very common."
"The dynamic programming problems solved in this course are typical interview questions, which is a big plus."
"Taking this course helped me gain confidence in solving dynamic programming problems during technical interviews."
"Perfect for interview prep, covers essential DP patterns."
Provides a clear framework for solving DP problems.
"The step-by-step approach taught in this course is incredibly useful and helps break down complex problems."
"I finally understood how to approach dynamic programming problems systematically thanks to the method provided."
"This course's step-by-step approach makes dynamic programming accessible and less intimidating."
"The systematic process shown was key to building my confidence in solving DP questions."
Could benefit from harder problems or deeper dives.
"Good introduction, but could use more in-depth coverage on complex topics or optimization techniques."
"Felt like it only scratched the surface; I was hoping for more challenging problems to truly 'master' DP."
"While the basics are covered well, it doesn't quite live up to the 'Master the art' title for advanced learners."
"Would love to see more complex or less common dynamic programming problems discussed."

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 Master the art of Dynamic Programming with these activities:
Review Recursion Fundamentals
Solidify your understanding of recursion, as it's a foundational concept for dynamic programming.
Browse courses on Recursion
Show steps
  • Study recursion examples in different programming languages.
  • Implement recursive solutions for simple problems like factorial or Fibonacci sequence.
  • Trace the call stack for recursive functions to understand how they work.
Solve Basic Backtracking Problems
Practice backtracking to build the intuition needed for dynamic programming.
Show steps
  • Solve backtracking problems like N-Queens or Sudoku solver.
  • Analyze the time complexity of your backtracking solutions.
  • Compare different backtracking strategies and their performance.
Implement a Memoization Decorator
Create a reusable memoization decorator to optimize recursive functions, a key technique in dynamic programming.
Show steps
  • Research memoization techniques and their benefits.
  • Implement a memoization decorator in Python or Java.
  • Test the decorator with various recursive functions.
  • Compare the performance of memoized and non-memoized functions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Work through Dynamic Programming Tutorials on LeetCode
Follow LeetCode tutorials to learn how to solve dynamic programming problems.
Show steps
  • Select a set of dynamic programming problems on LeetCode.
  • Work through the tutorials and understand the solutions.
  • Implement the solutions in your preferred programming language.
  • Analyze the time and space complexity of the solutions.
Create a Dynamic Programming Cheat Sheet
Summarize key dynamic programming concepts and techniques in a cheat sheet for future reference.
Show steps
  • Review the course materials and identify key concepts.
  • Organize the concepts into a cheat sheet format.
  • Include examples and code snippets to illustrate the concepts.
  • Share the cheat sheet with other students for feedback.
Read 'Dynamic Programming for Coding Interviews'
Study a book dedicated to dynamic programming to reinforce your understanding and learn new problem-solving techniques.
View Dynamic Programming for Coding Interviews: A... on Amazon
Show steps
  • Read the book and understand the concepts.
  • Solve the problems presented in the book.
  • Compare your solutions with the book's solutions.
  • Identify areas where you need more practice.
Review 'Introduction to Algorithms' (CLRS)
Consult a classic algorithms textbook to gain a deeper understanding of the theoretical underpinnings of dynamic programming.
View Introduction to Algorithms, fourth edition on Amazon
Show steps
  • Read the dynamic programming chapter in CLRS.
  • Understand the theoretical concepts presented in the chapter.
  • Solve the exercises at the end of the chapter.
  • Compare your solutions with the solutions manual.

Career center

Learners who complete Master the art of Dynamic Programming will develop knowledge and skills that may be useful to these careers:
Competitive Programmer
Competitive programmers participate in programming contests where they solve algorithmic problems under time constraints. This course is perfectly suited for aspiring competitive programmers since dynamic programming is a frequently tested topic in these contests. This course helps competitive programmers master recursion, backtracking, and step-by-step dynamic programming solution development. The detailed examples and exercises, along with the analysis of time and space complexities, are crucial for excelling in competitive programming challenges.
See salaries and explore the career path for Competitive Programmer
Algorithm Engineer
An algorithm engineer is a specialized software engineer that focuses on algorithm design and implementation. This course helps algorithm engineers by providing them with a deep understanding of dynamic programming. With the algorithms and data structures that are learned in this course, the algorithm engineer can master recursion, backtracking, and step-by-step approaches to dynamic programming solutions. The course’s focus on analyzing time and space complexities will also benefit the algorithm engineer by allowing them to optimize code.
See salaries and explore the career path for Algorithm Engineer
Algorithm Developer
Algorithm developers are responsible for designing and implementing algorithms to solve specific problems, often in areas like data science, machine learning, or optimization. This course directly supports the work of an algorithm developer by focusing on dynamic programming techniques. Given that dynamic programming is a core algorithmic paradigm, algorithm developers will find the detailed coverage of recursion, backtracking, and step-by-step problem-solving approaches invaluable for algorithm design. The practical examples and exercises in the course, along with the focus on time and space complexity analysis, all prepare an algorithm developer to create more efficient and effective algorithms.
See salaries and explore the career path for Algorithm Developer
Optimization Specialist
Optimization specialists focus on improving the efficiency and performance of systems, processes, or algorithms. This course is a great fit for the optimization specialist, because it provides in-depth knowledge of dynamic programming. The optimization specialist can learn recursion, backtracking, and step-by-step approaches to dynamic programming solutions. Also, the course’s emphasis on time and space complexity analysis can further help the optimization specialist to fully optimize and improve.
See salaries and explore the career path for Optimization Specialist
Software Engineer
A software engineer designs, develops, and tests software applications. This often involves creating algorithms and data structures to solve complex problems. This course helps software engineers by providing in-depth knowledge of dynamic programming, a powerful algorithmic technique frequently used in software development. Understanding recursion, backtracking, and step-by-step approaches to dynamic programming solutions, as taught in this course, can significantly enhance a software engineer's ability to optimize code and improve application performance at work. The course's focus on analyzing time and space complexities is also highly beneficial for efficient software design.
See salaries and explore the career path for Software Engineer
Game Developer
Game developers create video games, and this often involves solving complex algorithmic problems related to artificial intelligence, pathfinding, and resource management. This course can directly contribute to the success of a game developer by providing knowledge of dynamic programming techniques. A game developer will find the detailed explanation of recursion, backtracking, and step-by-step approaches invaluable for optimizing game performance and creating engaging gameplay experiences. The emphasis on time and space complexity analysis is crucial for developing games that run smoothly on various hardware platforms.
See salaries and explore the career path for Game Developer
Lead Programmer
Lead programmers are responsible for overseeing software development projects and guiding other programmers. This course helps lead programmers not just because it provides them with a knowledge of dynamic programming, but also because it teaches them how to analyze the time and space complexities of recursive solutions as well as dynamic programming solutions. With this knowledge the lead programmer can better guide their teammates towards efficient problem solving. With recursion, backtracking, and step-by-step approaches to dynamic programming solutions, lead programmers can better mentor.
See salaries and explore the career path for Lead Programmer
Machine Learning Engineer
Machine learning engineers design, build, and deploy machine learning models. While machine learning relies heavily on statistical techniques, algorithmic optimization plays a major role in model training and deployment. This course focusing on dynamic programming may be useful to a machine learning engineer, as knowledge of recursion, backtracking, and step-by-step approaches to dynamic programming solutions is especially helpful for optimizing model performance and resource utilization. The course's emphasis on time and space complexity analysis also contributes to efficient model deployment.
See salaries and explore the career path for Machine Learning Engineer
Data Scientist
Data scientists use statistical methods and algorithms to analyze data and extract insights. While data science encompasses a broad range of techniques, a strong understanding of algorithms, like those taught in this course, can be extremely valuable. This course may be useful to a data scientist, as it helps one to implement dynamic programming, recursion, backtracking, and step-by-step approaches to dynamic programming solutions. Such knowledge helps to solve optimization problems, develop efficient data processing pipelines, and build predictive models. Also, the ability to analyze time and space complexities of algorithms improves the efficiency of data science workflows.
See salaries and explore the career path for Data Scientist
Quantitative Analyst
Quantitative analysts, often working in finance, develop and implement mathematical models to analyze financial data, manage risk, and make trading decisions. Given that many financial problems can be formulated as optimization problems, dynamic programming, as covered in this course, may be useful. This course will help quants build a foundation in recursion, backtracking techniques, and the step-by-step approach to dynamic programming solutions. The ability to analyze time and space complexities of algorithms, as taught in the course, is also vital for developing efficient and reliable trading and risk management systems.
See salaries and explore the career path for Quantitative Analyst
Data Analyst
Data analysts examine data using statistical tools. While data analysts do not typically implement algorithms, this course can inform the tools and methods that are used in data analysis. Specifically, this course helps data analysts by building a foundation in dynamic programming, recursion, backtracking, and step-by-step approaches to dynamic programming solutions. The ability to analyze time and space complexities of algorithms may also improve the efficiency of data science workflows.
See salaries and explore the career path for Data Analyst
Business Intelligence Analyst
Business intelligence analysts identify the business trends of a company. While business intelligence analysts do not typically implement algorithms, this course may inform the data analysis they perform. Specifically, this course can help business intelligence analysts by building a foundation in dynamic programming, recursion, backtracking, and step-by-step approaches to dynamic programming solutions. The ability to analyze time and space complexities of algorithms helps to improve analytical accuracy.
See salaries and explore the career path for Business Intelligence Analyst
Math Educator
Math educators teach mathematics at various levels. This course can help math educators deepen their understanding of algorithmic problem solving which can then inform the lessons that they teach. Specifically, this course helps math educators build a foundation in dynamic programming, recursion, backtracking, and step-by-step approaches to dynamic programming solutions. The ability to analyze time and space complexities of algorithms improves the quality of instruction.
See salaries and explore the career path for Math Educator
Cloud Consultant
Cloud consultants advise organizations on cloud computing strategies. While cloud consultants don't typically implement algorithms, this course may inform the recommendations that they suggest. Specifically, this course can help cloud consultants by building a foundation in dynamic programming, recursion, backtracking, and step-by-step approaches to dynamic programming solutions. The ability to analyze time and space complexities of algorithms may help them choose the right cloud solution.
See salaries and explore the career path for Cloud Consultant
Financial Planner
Financial planners develop financial plans for individuals and families. While financial planners do not typically implement algorithms, this course may inform their financial recommendations. Specifically, this course may help financial planners by building a foundation in dynamic programming, recursion, backtracking, and step-by-step approaches to dynamic programming solutions. The ability to analyze time and space complexities of algorithms may help them choose the right investments.
See salaries and explore the career path for Financial Planner

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 Master the art of Dynamic Programming.
Cover image
Introduction to Algorithms, fourth edition
Save
Comprehensive textbook on algorithms, covering a wide range of topics including dynamic programming. It provides detailed explanations, pseudocode, and analysis of various dynamic programming techniques. It's a valuable resource for understanding the theoretical foundations and practical applications of dynamic programming, making it suitable as a reference or for deeper study.
Introduction to Algorithms, fourth edition
Hardcover
$$$$
Introduction to Algorithms, fourth edition
Kindle Edition
$$$
Cover image
Dynamic Programming For Coding Interviews
Save
Provides a comprehensive guide to dynamic programming problems commonly encountered in coding interviews. It covers a wide range of problems with detailed explanations and code examples. It is particularly useful for solidifying your understanding of dynamic programming techniques and preparing for technical interviews. This book adds depth to the course by providing many examples.
Dynamic Programming for Coding Interviews: A Bottom...
Paperback
$
Dynamic Programming for Coding Interviews: With...
Paperback
$$
Dynamic Programming for Coding Interviews: A Bottom...
Kindle Edition
$
Dynamic Programming for Coding Interviews: With...
Kindle Edition
$

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Rating
4.3 Filled star Filled star Filled star Filled star Empty star
Effort
4.5 total hours
Via
Udemy
Instructor
Ajay Prakash
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Offers a step-by-step approach to dynamic programming, which can be helpful for coding interviews
Provides detailed solutions with code in both Java and Python, which are widely used in software development
Explores recursion and backtracking techniques, which are fundamental concepts in computer science curricula
Examines time and space complexities of recursive and dynamic programming solutions, which is crucial for algorithm optimization
Focuses on dynamic programming problems, which are frequently encountered in software engineering and algorithm design
Requires learners to understand recursion in depth, which may require additional study for some learners
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