The Best Order to learn Algorithms & Data Structures?
Summary
TLDRThe 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
📚 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.
Mindmap
Keywords
💡DSA
💡Roadmap
💡Depth-first search (DFS)
💡Breadth-first search (BFS)
💡Optimization
💡Algorithms
💡Data Structures
💡Prerequisites
💡Dynamic Programming
💡Bit Manipulation
💡Math
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
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