Dynamic Programming Python, Coding Interviews & Applications from Udemy

Have you ever wondered what makes a good developer? Why it is that big tech companies are increasingly asking candidates to solve challenging coding puzzles in interviews? Or why you should bother to learn about complicated algorithms?

With regards to technical skills a good developer has an understanding of computer science and knows when to apply this knowledge. Tech companies know that if someone has a good grasp of these fundamentals, she will likely be fine learning any programming language, using any new tool and solving a wide range of programming problems. As a developer comprehending data structures and algorithms you’ll be better equipped to tackle some of the more difficult problems both in your day-to-day job and for coding interviews.

Dynamic Programming is a topic in data structures and algorithms. It covers a method (the technical term is “algorithm paradigm”) to solve a certain class of problems. In this course we will go into some detail on this subject by going through various examples. The course is designed not to be heavy on mathematics and formal definitions. Instead you will learn through practical everyday programming algorithms and through some coding interview puzzles. We present a method to recognize problems that can be solved using dynamic programming and then build an efficient solution through small gradual steps.

In addition, you will also learn how to gamble professionally, how to be an air traffic controller and how to become a serious writer.*

*Not really… but hey it’s hard to make Dynamic Programming sound exciting...

All code in this course can be found on github, username/project: cutajarj/DynamicProgrammingInPython

At the end of the course we have a small coding exercise to test your knowledge.

What's inside

Learning objectives

Recognize a problem that can be solved using dynamic programming
Come up with both a top down and bottom up dynamic programming solution using python
Use dynamic programming for coding interview puzzles and practical applications

Improve your problem-solving skills and become a better developer
Revise your recursion knowledge

Recognize a problem that can be solved using dynamic programming
Come up with both a top down and bottom up dynamic programming solution using python
Use dynamic programming for coding interview puzzles and practical applications
Improve your problem-solving skills and become a better developer
Revise your recursion knowledge

Syllabus

Introduction

Useful links and resources

Understanding Recursion

Introduction to recursion

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Covers dynamic programming, which is a method to solve a certain class of problems, making it highly relevant for developers

Teaches how to recognize problems that can be solved using dynamic programming, which is useful for coding interviews and practical applications

Uses Python, a popular language in software engineering, making it easier for developers to apply dynamic programming concepts

Includes a small coding exercise to test knowledge, which is standard practice in many coding courses and bootcamps

Requires learners to revise their recursion knowledge, which may be a barrier for some students without prior experience

Includes code on Github, which requires learners to have a Github account and familiarity with Git version control

Reviews summary

Dynamic programming for coding interviews

According to students, this course is a highly effective way to learn Dynamic Programming (DP), especially for preparing for coding interviews. Many appreciate the instructor's ability to explain complex concepts clearly, focusing on intuition and practical application rather than theoretical depth. The use of Python examples is frequently mentioned as a positive. While largely praised for its approachability, some found that it could benefit from more practice problems or deeper dives into certain topics.

Assumes prior understanding of recursion.

"A strong understanding of recursion is definitely needed before taking this course."

"The initial recursion review was helpful, but you really need prior recursive problem-solving experience."

"It's important to be comfortable with recursion and basic algorithms beforehand."

Good for introduction, perhaps less for advanced.

"This course is an excellent introduction to DP, perfect for beginners or intermediate learners."

"For someone with some DP background, it might feel a bit basic in parts, but still a good refresher."

"Could use more in-depth coverage on very complex DP problems or advanced techniques."

Uses practical Python examples effectively.

"Using Python for the examples made the code easy to follow and understand."

"I really liked the hands-on coding examples in Python; they helped solidify my understanding."

"The practical examples provided were very helpful in seeing DP in action with Python."

Highly relevant for coding interview preparation.

"This course was a great help in my coding interview preparation, covering common DP patterns effectively."

"The problems discussed are very relevant to what you might encounter in technical interviews."

"If you are preparing for interviews, this course provides a solid foundation in dynamic programming."

Instructor breaks down complex DP concepts well.

"The instructor is great at explaining the concepts and breaking down complex dynamic programming problems into understandable steps."

"I appreciate how the lecturer simplified DP, which is typically seen as a very difficult topic."

"He explains the intuition behind Dynamic Programming really well, which helps in tackling new problems."

Could benefit from additional practice problems.

"I wish there were more practice problems or exercises to reinforce the concepts learned."

"While the explanations are great, adding more varied problems to solve would make it perfect."

"More challenges or homework assignments would be beneficial after each section."

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 Dynamic Programming Python, Coding Interviews & Applications with these activities:

Review Recursion Fundamentals

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Solidify your understanding of recursion, as dynamic programming relies heavily on recursive thinking.

Browse courses on Recursion

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Review the definition of recursion and its applications.
Practice writing simple recursive functions (e.g., factorial, Fibonacci).
Identify the base case and recursive step in each function.

Review 'Introduction to Algorithms'

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Use this book to deepen your understanding of the algorithms discussed in the course.

View Introduction to Algorithms, fourth edition on Amazon

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Read the chapters related to dynamic programming and recursion.
Work through the examples and exercises provided in the book.
Compare the book's approach to the course's approach.

Review 'Cracking the Coding Interview'

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Use this book to prepare for coding interviews by practicing dynamic programming problems.

View Cracking the Coding Interview: 189 Programming... on Amazon

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Read the dynamic programming section of the book.
Solve the dynamic programming problems presented in the book.
Analyze the solutions and understand the underlying concepts.

Four other activities

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Show all seven activities

Create a Dynamic Programming Tutorial

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Deepen your understanding by explaining dynamic programming concepts to others.

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Choose a specific dynamic programming topic or problem.
Research the topic thoroughly.
Create a tutorial that explains the topic in a clear and concise manner.
Include examples and code snippets to illustrate the concepts.

Solve Dynamic Programming Problems on LeetCode

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Reinforce your understanding of dynamic programming by solving practical coding problems.

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Create a LeetCode account (if you don't already have one).
Search for dynamic programming problems on LeetCode.
Solve at least 5 dynamic programming problems of varying difficulty.
Analyze your solutions and compare them to other solutions.

Implement a Dynamic Programming Solution for a Real-World Problem

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Apply your dynamic programming skills to solve a practical problem, solidifying your understanding and demonstrating your abilities.

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Identify a real-world problem that can be solved using dynamic programming (e.g., optimizing resource allocation, finding the shortest path).
Design a dynamic programming solution for the problem.
Implement the solution in Python.
Test and debug your implementation.
Document your project, explaining the problem, your solution, and the results.

Tutor other students in Dynamic Programming

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Reinforce your understanding of dynamic programming by teaching it to others.

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Offer to tutor classmates or other students who are struggling with dynamic programming.
Prepare explanations and examples to help them understand the concepts.
Answer their questions and provide guidance.

Career center

Learners who complete Dynamic Programming Python, Coding Interviews & Applications will develop knowledge and skills that may be useful to these careers:

Competitive Programmer

Competitive programmers participate in programming contests, solving algorithmic puzzles under time constraints. This course is tailor-made for competitive programmers, providing essential dynamic programming knowledge and problem-solving skills. The course's focus on coding interview puzzles and practical applications is directly relevant to the types of challenges faced in programming contests. The course's coverage of recursion, top-down and bottom-up dynamic programming, and optimization techniques is precisely the training needed to excel in competitive programming, especially the module on optimization.

See salaries and explore the career path for Competitive Programmer

Algorithm Developer

Algorithm developers design and implement algorithms for various applications, such as search engines, recommendation systems, and financial modeling. A deep understanding of dynamic programming is crucial for optimizing algorithm performance. This course directly addresses the needs of an algorithm developer, providing the ability to recognize problems suitable for dynamic programming and build efficient solutions. The course's coverage of recursion, top-down and bottom-up dynamic programming, and optimization techniques are extremely relevant. The course's focus on coding interview puzzles is directly related to the kinds of problems an Algorithm Developer works on.

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Software Engineer

A software engineer designs, develops, and tests software applications. They need to be adept in algorithms and data structures to build efficient and scalable solutions. This course helps in recognizing problems that can be solved using dynamic programming and constructing solutions through gradual steps. For any software engineer, mastering dynamic programming is an indispensable tool. The course's practical approach to learning algorithms, coding interview puzzles, and application of Python make it particularly helpful. The modules on recursion and optimization are beneficial for software engineers to write clean, efficient code. This course builds a foundation in skills that are useful in day to day work as a Software Engineer.

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Bioinformatician

Bioinformaticians analyze biological data using computational techniques. Dynamic programming is a core technique in bioinformatics, used for sequence alignment, phylogenetic tree construction, and protein structure prediction. This course directly addresses the needs of bioinformaticians, providing a strong foundation in dynamic programming and its applications. The course's focus on problem-solving skills, algorithm design, and practical coding exercises is highly relevant. In particular, the modules on string distance and recursion are directly applicable to bioinformatics problems. A bioinformatician typically requires an advanced degree.

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Back-End Developer

A back end developer is a type of computer programmer who focuses on servers, databases, and application programming interfaces. Using dynamic programming, a Back End Developer can solve problems that arise on the server. This course helps to recognize problems that can be solved using dynamic programming and constructing solutions through gradual steps. For any back end developer, mastering dynamic programming is an indispensable tool. The course's practical approach to learning algorithms and application of Python make it particularly helpful when creating back end solutions.

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Educator

Educators working at the college or university level are often tasked with teaching data structures and algorithms. This course allows an educator to hone their understanding of dynamic programming, which is often a challenging subject to teach. By taking this course, Educators have the opportunity to recognize problems that can be solved using dynamic programming and constructing solutions through gradual steps. The course's practical approach to learning, coding interview puzzles, and the application of Python make it particularly helpful for conveying the course's knowledge to a classroom.

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Research Scientist

Research scientists conduct research in various fields, often involving the development of new algorithms and techniques. A research scientist may find this course useful if their research involves optimization problems or algorithmic development that can benefit from dynamic programming. The course's focus on problem-solving skills, algorithm design, and the practical application of dynamic programming is helpful. This course provides a structured approach to learning and applying dynamic programming techniques. A research scientist typically requires an advanced degree.

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Quantitative Analyst

Quantitative analysts, often working in the finance industry, develop and implement mathematical models for pricing derivatives, managing risk, and optimizing trading strategies. They require a strong foundation in algorithms and optimization techniques. This course is useful as quantitative analysts may find dynamic programming valuable in solving optimization problems related to portfolio allocation, option pricing, and algorithmic trading. The course's emphasis on recognizing problems solvable with dynamic programming and building efficient solutions can be directly applied to quantitative finance. The modules on maximum sub array and optimization are also directly applicable.

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Machine Learning Engineer

Machine learning engineers are responsible for developing and deploying machine learning models. They need to be proficient in algorithms and data structures to optimize model performance and scalability. A machine learning engineer may find this course helpful, as dynamic programming can be applied to solve problems in areas such as reinforcement learning and sequence alignment. The course helps to recognize problems that can be solved using dynamic programming to make models more efficient. Moreover, the discussions of recursion, top-down and bottom-up approaches, and coding exercises are valuable for Machine Learning Engineers.

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Data Scientist

Data scientists analyze large datasets to extract meaningful insights and develop predictive models. They frequently use algorithmic techniques to optimize performance and solve complex problems. This course may be useful for data scientists who need to implement dynamic programming solutions for optimization problems or create efficient algorithms for data analysis. The course's emphasis on problem-solving skills and practical applications would be valuable. Moreover, the exploration of recursion, top-down and bottom-up approaches, and coding exercises enhances a data scientist's ability to tackle intricate data challenges. In particular, the modules on maximum sub array, text justification, and string distance have direct applications to the kinds of problems a Data Scientist faces.

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Game Developer

Game developers create video games for various platforms. They use algorithms and data structures to implement game mechanics, artificial intelligence, and graphics. This course may be useful for game developers, as dynamic programming can be applied to optimize game AI, pathfinding algorithms, and resource management. The course's focus on problem-solving skills and practical applications would be valuable in addressing performance bottlenecks in game development. The modules on recursion, top-down and bottom-up approaches, and coding exercises are also relevant. This course would be especially useful to a Game Developer working on turn-based strategy games.

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Application Developer

An Application Developer is involved in the creation, testing, and maintenance of applications, often working with specific languages and platforms. Application developers need a strong understanding of efficient coding practices and problem-solving skills to design effective applications. This course may be helpful for application developers as it provides a structured approach to understand when to apply dynamic programming, through practical everyday programming algorithms and coding interview puzzles. Practical applications, like text justification and string distance, are common problems Application Developers may face.

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Data Engineer

Data engineers build and maintain the infrastructure for data storage, processing, and analysis. While they may not directly implement complex algorithms, they need to optimize data pipelines and ensure efficient data processing. This course may be useful for data engineers as dynamic programming techniques can sometimes be applied to optimize data processing workflows. The course's focus on problem-solving skills and understanding algorithmic paradigms is valuable. Moreover, the coverage of recursion and optimization can help data engineers write more efficient code for data manipulation tasks. The module on maximum sub array also has direct applications to data processing.

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Technology Consultant

Technology consultants advise organizations on how to use technology to achieve their goals. They need a broad understanding of various technologies and problem-solving skills. Technology consultants may find this course helpful, as dynamic programming can be a valuable tool for optimizing processes and solving complex business problems. The course's focus on problem-solving skills and practical applications of dynamic programming is relevant. The modules on text justification and string distance may have direct applications.

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Intelligence Analyst

Intelligence analysts collect and analyze information to identify threats and provide insights to decision-makers. They often use algorithms and data analysis techniques to process large amounts of data. This course may be useful for intelligence analysts, as dynamic programming can be applied to solve problems such as sequence alignment, pattern recognition, and optimization of resource allocation. The course's focus on problem-solving skills and practical applications is valuable, especially recursion and optimization. The module on string distance may be useful.

See salaries and explore the career path for Intelligence Analyst

Dynamic Programming Python, Coding Interviews & Applications

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Reviews summary

Dynamic programming for coding interviews

Activities

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