The Best Order to learn Algorithms & Data Structures?

Summary

The speaker suggests an optimal approach to learning Data Structures and Algorithms (DSA) by advocating for breadth-first search (BFS) over depth-first search (DFS). They argue that BFS allows for a more logical progression, starting with common and foundational concepts, and avoids the pitfalls of tackling rare or challenging topics prematurely. The speaker emphasizes the importance of learning about basic structures like stacks and linked lists before diving into more complex topics like tries and advanced graphs. They also recommend prioritizing dynamic programming, bit manipulation, and math towards the end of the learning path, as these are less likely to appear in interviews but are still valuable skills to master.

Takeaways

• 📈 The optimal strategy for learning Data Structures and Algorithms (DSA) is akin to an algorithmic question, specifically whether to use depth-first search (DFS) or breadth-first search (BFS) traversal.
• 🌐 BFS is recommended for the DSA learning path because it systematically covers broader concepts before delving into more specialized topics.
• 🔄 The script suggests that the most common concepts in DSA are at the beginning of the learning path, which aligns with the BFS approach.
• 🔄 A DFS approach might lead to learning rarer topics like tries before more foundational concepts like stacks, which is not ideal.
• 🔄 The script warns against a non-optimal DFS path that could lead to learning about dynamic programming (DP) before understanding graphs, which is a more challenging order.
• 🔄 BFS allows for a more structured progression, covering topics in layers, which helps in building a solid foundation before moving on to advanced topics.
• 🔄 The script advises prioritizing certain topics in the later stages of the learning path, such as dynamic programming, bit manipulation, and math.
• 🔄 The learning path should consider the prerequisites of topics, ensuring that foundational topics are covered before tackling more advanced ones.
• 🔄 The script implies that some topics may be less likely to appear in interviews, but they are still worth learning for a comprehensive understanding.
• 🔄 The speaker's personal preference for the learning path is to learn dynamic programming before moving on to bit manipulation and math, which are considered less likely to come up in interviews.

Q & A

What is the primary question being addressed in the script?

The primary question is about the optimal progression through a Data Structures and Algorithms (DSA) learning roadmap, specifically whether to follow a depth-first or breadth-first approach.

Why is the choice between depth-first search (DFS) and breadth-first search (BFS) relevant to the DSA learning progression?

The choice is relevant because it determines the order in which concepts are learned, which can affect the learner's ability to grasp more complex topics and their application in interviews.

What are the potential drawbacks of using a depth-first search approach in learning DSA?

DFS could lead to learning rarer topics like tries before more common ones like stacks, which might not be the most efficient order. It could also result in learning about advanced topics without a solid foundation in prerequisite concepts.

What is the main argument for using a breadth-first search approach in DSA learning?

BFS allows learners to cover common concepts at the beginning, which are more likely to come up in interviews, and it helps in building a stronger foundation before tackling more advanced topics.

How does the script suggest prioritizing topics in the latter part of the learning roadmap?

The script suggests prioritizing dynamic programming, bit manipulation, and math, as these topics are less likely to come up in interviews but are important for a comprehensive understanding of DSA.

What is the significance of understanding the prerequisites for certain topics in DSA learning?

Understanding prerequisites ensures that learners have the necessary foundational knowledge before tackling more advanced topics, which can prevent confusion and improve the overall learning experience.

Why might learning about tries before stacks be considered suboptimal in the DSA learning progression?

Tries are a more specialized and less commonly encountered data structure compared to stacks. Learning about tries before more fundamental data structures like stacks could lead to a less effective learning path.

What is the role of backtracking in the context of the DSA learning progression?

Backtracking is an important concept in algorithm design, but it is considered a more advanced topic. The script suggests that it might be better to learn about it after establishing a strong foundation with more common data structures and algorithms.

How does the script address the idea of learning advanced graphs without prior knowledge of regular graphs?

The script advises against learning advanced graphs before regular graphs because certain topics have multiple prerequisites, and it's important to build a solid understanding of the basics before moving on to more complex subjects.

What is the script's stance on the order in which to learn about dynamic programming, bit manipulation, and math?

The script suggests learning dynamic programming first, followed by bit manipulation, and then math, as these topics are important but less likely to be encountered in interviews.

Outlines

00:00

📚 Optimal DSA Progression Strategy

The paragraph discusses the best approach to learning Data Structures and Algorithms (DSA) by comparing depth-first search (DFS) and breadth-first search (BFS) strategies. It suggests that BFS might be more optimal due to the roadmap's structure, which doesn't clearly indicate the commonality of concepts. The author advises against learning rare topics like tries before mastering foundational ones like stacks. The paragraph also highlights the importance of learning certain topics in a logical order, such as regular graphs before advanced graphs, and suggests a prioritized learning path that includes dynamic programming, bit manipulation, and math.

Keywords

💡DSA

DSA stands for Data Structures and Algorithms, which is a core topic in computer science. It involves organizing and storing data in an efficient manner and developing algorithms to perform operations on that data. In the video, the speaker discusses the optimal progression through a DSA learning roadmap, which is a guide to mastering these concepts.

💡Roadmap

A roadmap in the context of the video refers to a structured plan or guide that outlines the sequence of topics one should learn to effectively understand and apply DSA concepts. The speaker suggests that a breadth-first search approach to the roadmap is more optimal than a depth-first search, as it allows for a more balanced and comprehensive understanding of the foundational concepts before delving into more advanced topics.

💡Depth-first search (DFS)

Depth-first search is an algorithm for traversing or searching tree or graph data structures. It starts at the root node and explores as far as possible along each branch before backtracking. The speaker uses DFS as an analogy for a learning approach that might lead to a less efficient path through the DSA roadmap, as it could cause learners to encounter complex topics before they have a solid grasp of the basics.

💡Breadth-first search (BFS)

Breadth-first search is another algorithm for traversing or searching tree or graph data structures, but it explores all of the neighbor nodes at the present depth before moving on to nodes at the next depth level. The speaker recommends BFS as a learning strategy for the DSA roadmap because it allows learners to cover a wide range of foundational concepts before moving on to more advanced topics, which is considered more efficient and logical.

💡Optimization

In the context of the video, optimization refers to the process of choosing the most effective and efficient way to learn and understand DSA concepts. The speaker discusses the optimization of learning paths, suggesting that a breadth-first approach is more optimal than a depth-first approach for navigating the DSA roadmap.

💡Algorithms

Algorithms are step-by-step procedures or formulas for solving problems. They are fundamental to computer science and are the basis for many operations in data structures. The video script mentions algorithms in the context of learning strategies, emphasizing the importance of understanding algorithms like DFS and BFS before applying them to the learning process.

💡Data Structures

Data structures are ways of organizing and storing data so that it can be accessed and used efficiently. Common data structures include arrays, linked lists, stacks, queues, trees, and graphs. The video script discusses the importance of learning about foundational data structures like stacks and linked lists before moving on to more complex topics like tries and advanced graphs.

💡Prerequisites

Prerequisites are conditions or requirements that must be met before one can proceed with a particular task or topic. In the video, the speaker mentions that certain DSA topics have multiple prerequisites, meaning that learners should have a solid understanding of certain foundational concepts before attempting to learn more advanced material.

💡Dynamic Programming

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems and storing the solutions to these subproblems to avoid redundant computations. It is a key concept in algorithms and is mentioned in the video as a topic that should be learned after establishing a strong foundation in basic data structures and algorithms.

💡Bit Manipulation

Bit manipulation refers to the process of directly operating on the binary representation of data. It is a technique used in computer programming to perform operations like counting, reversing, and searching within the binary structure of data. The video script suggests that bit manipulation is a topic that learners should consider after mastering dynamic programming, indicating its complexity and the need for a strong understanding of the underlying binary system.

💡Math

Mathematics plays a crucial role in computer science, particularly in algorithms and data structures. It provides the theoretical foundation for many concepts and helps in understanding and solving problems more efficiently. The speaker recommends learning math towards the end of the DSA progression, possibly because it often involves more abstract concepts that build upon the foundational knowledge acquired earlier.

Highlights

The best Data Structures and Algorithms (DSA) progression is akin to an algorithms question.

Optimality in learning DSA is compared to choosing between depth-first search (DFS) and breadth-first search (BFS) traversal strategies.

Breadth-first search (BFS) is recommended for learning DSA due to its structure.

Common concepts in DSA are at the beginning of the learning path, which is not clearly indicated in the roadmap.

A depth-first search approach might lead to learning rarer topics like tries before essential ones like stacks.

Learning about tries before stacks is not considered smart due to the complexity and relevance of the topics.

BFS allows for a more systematic approach by covering topics in layers, which is beneficial for understanding prerequisites.

DFS can lead to a challenging order of learning, such as tackling dynamic programming before graphs.

Regular graphs should be learned before advanced graphs due to their interconnected prerequisites.

Once in the second portion of the learning graph, the order becomes less predictable, and certain topics may be less likely to appear in interviews.

Dynamic programming, bit manipulation, and math are suggested as later topics to learn in the DSA progression.

The transcript suggests a strategic approach to learning DSA to maximize efficiency and understanding.

The learning roadmap for DSA does not always present topics in the most logical or easy-to-understand order.

The transcript emphasizes the importance of learning foundational concepts before diving into more advanced or specialized topics.

The transcript provides a practical guide for learners to navigate the DSA learning path more effectively.

Transcripts

what's the best DSA progression should I

go through each road map node in order

the question you're asking is how to go

through these which order but what you

don't realize is you're actually asking

an algorithms question you're asking

what would be more optimal here a depth

for search traversal or a breadth for

search traversal and it's funny because

you may not even know what these

algorithms are until you get to this

node or this node it's a very good

question but in my humble opinion breath

for search is probably the way to go

because one thing this road map does not

really clearly indicate is that the most

common concepts are actually at the

beginning take the alternative consider

this a proof by contradiction imagine a

depth first search traversal you do this

you do this you do this and then you do

this and then you you go here you've

covered four very good topics and then

you don't realize it but now you're

learning about tries it's a pretty rare

topic it's not super hard but it's

challenging and it's not smart in my

opinion to learn about tries before

you've learned about Stacks why would

you do that siding window and linked

list these are easier and they're

probably more likely to come up with a

bread research alternatively and and we

can continue with the DFS you'll get to

like heat you'll get to intervals not

bad topics but backtracking is pretty

big and you know depending on the order

you might have gone in this order you

might have gone in like the hardest

possible order where you learned about

DP before you learned about graphs I

wouldn't do that another reason with DFS

if you went like this path and you got

all the way down here you might get to

advanc graphs but you haven't even done

this graph this isn't actually a tree

because certain topics like this have

multiple prerequisites you should

probably do regular graphs before you do

Advanced graphs so another reason why

BFS is the approach here you do this you

do this layer then you do this layer

then here and then you can do this

though I will say some of these are less

likely like if once you get to this

second portion of the graph I would

prioritize this this this maybe this and

then probably this and this and then

this this at this point it becomes kind

of random like these are probably least

likely to come up in your interview but

I would learn dynamic programming and

then maybe bit manipulation and then

math so that's how I would do it