Nice ... in my advanced graduate algorithms course now ... half-way to MS in CS at age 63. If you're in CS you will have to know all of those. Basic data structures. Great vids as always.
I am in my final year of my CS degree and I am going back to relearn a lot of basics because I half assed my way to where I am but I have grown to like the program and want to be competent. Tho don’t tell my boss
I remember watching 1 or 2 of ur vids 8 months ago and i lost interesst and couldnt find the value.. now after hardcore 8 months of studying and practicing programing, system management and recently math.. i think this is the most valuable YT channel ive seen.. obv im not comparing it to freecode camp.. but obv ur not in the same nieche .. just went trough clearing the nonvaluable yt channels ive subscribed too and u are one of the fews i left there.. super cool, inspiring, relatable, and informative ofc!! :)
Consider these actionable insights from the video: 1. Learn about arrays to store collections of data of the same type. 2. Explore linked lists when you need to efficiently insert or delete elements in a list. 3. Understand stacks if your project requires managing data in a last-in-first-out manner. 4. Utilize queues to process data in a first-in-first-out order, like managing tasks. 5. Implement hash tables to store and retrieve data using key-value pairs for fast access. 6. Master trees for representing hierarchical data and conducting efficient searches. 7. Dive into heaps when you need to find the minimum or maximum value efficiently. 8. Study graphs to represent and analyze relationships between objects or entities.
“Throw a hash map at it and hope for the best” I gave my friend that advice when we were both looking for a job our senior year of college. He told me a few weeks later that he was in an interview and had no idea what to do so he pulled out the hash map quote. The interviewer didn’t even bother having him implement it, they just said alright you sound like you know the approach we can continue
It’s worth noting, that Python also allows storing different data types in a single list. In fact, this capability is common to all dynamically typed languages.
And many statically typed languages which have a large runtime (like go for instance) have the same ability, in go you can just do this: var some [4]interface{}
this is not actually true. python lists (and other containers) store pointers to any python type. to the user it seems as though a list can store multiple type simultaneously but in reality the type it stores is always a pointer to a python object. storing fixed size data is very important because you can easily calculate it’s memory location from the index and the list start. if you cannot guarantee the size it’s a linear search. for example see why you cannot just index into UTF-8 strings
@@soothsayer1interfaces in the golang runtime are always pointers, for the same reason noted above. if you want an array with multiple types of elements, the way without using a pointer is a union type. the rust enum implementation is one such example
@@simpletongeek a union is a block of memory that can be interpreted as multiple types at the same time. so you can have a 4 byte int and a byte 4 array simultaneously. an interface is a pointer to a block of memory. the interface is also an agreement about the methods that can be run on it. this way you can call the same methods without knowing which type it is. but the important bit, is that an interface is only one of it’s types while a union is simultaneously all of it’s types
Hey Forest! I’ve been following your videos for a while now, and I really love your content. I would greatly appreciate it if you could make a video about Spring Boot.
@@TurdBoi-tf5lf reallocating a bigger/potentially smaller array as needed outperforms Linked lists under pretty much any aspect. You can also just double it when increasing and you would still use the same amount of memory than a linked list even if you don't use the elements. That's the reason why a lot of languages e.g. Java have ArrayLists and not LinkedLists as a standard List implementation. Lists are slower under pretry much any aspect, they use more memory and they often are trickier to work with... why do we use linked lists? Oh btw, adding a lot of things or removing a lot of things is still slower for Linked lists than arrays since you have a lot of syscalls (which is pretty slow overall) And python decided to use Linked lists instead of arrays 🤡
@@TurdBoi-tf5lf oof, I was sure I elaborated already. Anywhays, Arrays use less memory, are faster to exoand and even faster to iterate over. As a matter off acts, iterating over an array of less than 5k elements in C is generally faster than using a hashmap. The memory is self explanatory, the array has a fat pointer to the start (or normal pointer) and then you go trough it. The Linked list has generally around double the memory consumption (pointer to next address which generally is 64-bit number, and the value (often also a pointer). Now surprisingly normal arrays are often faster when expanding as well because you only have 1 (or maybe 2) system calls for expanding (normally doubling) the content in the array. For linked lists you have a syscall per item. So lists are slower add a lot of data, they are slower to iterate over, have no random access and consume around half the memory. This is alos the reason why pretty much every langiage that takes performance even remotely seriously implements lists as ArrayLists (Dynamic Arrays, vectors, call them how you like), and not as LinkedLists. Also, most algorithms on linked lists are significantly slower than arrays. Small bonus. Iteration over arrays is so fast because your CPU doesn't just load the data it needs from the RAM into it's cache, but it loads memory blocks. When it loads a memory block of array data, it automatically loads quite a few elements of the array, and therefore has more cache hits. LinkedLists are across the heap, therefore cache hits are honestly more the exception than the rule
As a JS, TS and Go Web Developer... I am yet to find the usage of most of these data structures. I have used arrays, list, hash tables or tables.. but apart from these ones, I haven't seen the usage of others.
Heaps can be quite valuable in e-commerce for tracking the top k most sold items. This is a useful metric for identifying where to focus your efforts and also helps in creating bestseller lists or targeted promotions for customers.
What about the DOM, that's a tree data structure with references to it in CSS with selectors. If you make a chat function with a queue with a fifo buffer for retry/ack functionality etc. Sometimes you may need to use open street maps, the geo data it contains is not always key/value, it's also a tree of json data that needs to be traversed. You can do that recursively, then you need to know about the underlaying programming language stack handling. If the recursion goes too deep you run into issues. JS and other language and frameworks tend to hide all that but it's good to know that they're there and you're actually using them.
I really think we should be cautious about "things that every programmer should know". In a computer science cursus you learn lots of concepts and stuffs, but it depends on what branche you'll be working in after graduating. For instance as a web dev, you'll mostly only work with an array. If you do work with lower level langages like C, then you might use more sophisticated data structures to use less memory or less cpu. Si to people desiring to switch job and start programming, Please ask round you what data structures are really used And save your time.
Uh the good old c-queue that is actually an array and goes lightning fast, if you only use bit manipulation, because your CPU does a quadrillion of those before it read in a line of RAM, completely obliterating any O() notation when you measure the actuall execution time in many cases.
The one point I disagree with is about the definition of an array. Same type for each slot and contiguous memory storage, yes, but not statically sized. And there's a lot more complexity to hash tables if you care to make a more complete description there. You have different hashing algorithms, balancing schemes, and even storage methods. For instance, in one implementation, I made the storage backing for a hash table as a balanced tree and each node was also part of an interlaced series of trees that were the chains. It used up a lot more memory than either a tree or a hash table alone, but it was an interesting experiment in maintaining sorted order while yielding slightly faster searches.
I was wondering if you may go deeper in another video series for each data structure with a coding example on how to build them and where we can find them in real world applications?
I'm a dev from india and this is true. During the recent layoffs they cleared out all of US/UK offices except the sales folks. Now that the feds have slashed interest rates they have started hiring here in the Bangalore office
Nice ... in my advanced graduate algorithms course now ... half-way to MS in CS at age 63. If you're in CS you will have to know all of those. Basic data structures. Great vids as always.
Wow, great. It's commendable. Do you feel anything different than say you were studying in your 20s? Keep it up, sir.
I am in my final year of my CS degree and I am going back to relearn a lot of basics because I half assed my way to where I am but I have grown to like the program and want to be competent.
Tho don’t tell my boss
Wow, I wasn't expecting the UA-cam algorithm to suggest I learn about data structures from a gas station clerk.
I remember watching 1 or 2 of ur vids 8 months ago and i lost interesst and couldnt find the value.. now after hardcore 8 months of studying and practicing programing, system management and recently math.. i think this is the most valuable YT channel ive seen.. obv im not comparing it to freecode camp.. but obv ur not in the same nieche .. just went trough clearing the nonvaluable yt channels ive subscribed too and u are one of the fews i left there.. super cool, inspiring, relatable, and informative ofc!! :)
My guy finished a day in the coal mines and came home to teach data structures! (can I get a link to that flannel? :D )
Consider these actionable insights from the video:
1. Learn about arrays to store collections of data of the same type.
2. Explore linked lists when you need to efficiently insert or delete elements in a list.
3. Understand stacks if your project requires managing data in a last-in-first-out manner.
4. Utilize queues to process data in a first-in-first-out order, like managing tasks.
5. Implement hash tables to store and retrieve data using key-value pairs for fast access.
6. Master trees for representing hierarchical data and conducting efficient searches.
7. Dive into heaps when you need to find the minimum or maximum value efficiently.
8. Study graphs to represent and analyze relationships between objects or entities.
“Throw a hash map at it and hope for the best”
I gave my friend that advice when we were both looking for a job our senior year of college.
He told me a few weeks later that he was in an interview and had no idea what to do so he pulled out the hash map quote. The interviewer didn’t even bother having him implement it, they just said alright you sound like you know the approach we can continue
ladies and gentlemen i think my brain committed suicide; but it's the exact information i need to solve a problem so it's well worth
Excellent presentation. You have a new subscriber (me).
I do learn quite a lot from your great tutorials 📖💻 Thank you for providing such great content 😊
It’s worth noting, that Python also allows storing different data types in a single list.
In fact, this capability is common to all dynamically typed languages.
And many statically typed languages which have a large runtime (like go for instance) have the same ability, in go you can just do this: var some [4]interface{}
this is not actually true. python lists (and other containers) store pointers to any python type. to the user it seems as though a list can store multiple type simultaneously but in reality the type it stores is always a pointer to a python object.
storing fixed size data is very important because you can easily calculate it’s memory location from the index and the list start. if you cannot guarantee the size it’s a linear search. for example see why you cannot just index into UTF-8 strings
@@soothsayer1interfaces in the golang runtime are always pointers, for the same reason noted above. if you want an array with multiple types of elements, the way without using a pointer is a union type. the rust enum implementation is one such example
Why does that sound like Unions in C?
@@simpletongeek a union is a block of memory that can be interpreted as multiple types at the same time. so you can have a 4 byte int and a byte 4 array simultaneously. an interface is a pointer to a block of memory. the interface is also an agreement about the methods that can be run on it. this way you can call the same methods without knowing which type it is. but the important bit, is that an interface is only one of it’s types while a union is simultaneously all of it’s types
I like this guy 😅
I don’t want to escape Java
Lewis Melissa Robinson Melissa Robinson Gary
where did you buy that keyboard?
Fixed size arrays are boring
Thanks dude! super helpful
Hey Forest! I’ve been following your videos for a while now, and I really love your content. I would greatly appreciate it if you could make a video about Spring Boot.
wtf am i watching
Great video.
Could you do a video about the job market situation currently?
Thx!
ah yes, we are in 2024 and we still like to pretend that linked lists have a reason to exist -.-
Elaborate
@@TurdBoi-tf5lf reallocating a bigger/potentially smaller array as needed outperforms Linked lists under pretty much any aspect. You can also just double it when increasing and you would still use the same amount of memory than a linked list even if you don't use the elements. That's the reason why a lot of languages e.g. Java have ArrayLists and not LinkedLists as a standard List implementation. Lists are slower under pretry much any aspect, they use more memory and they often are trickier to work with... why do we use linked lists?
Oh btw, adding a lot of things or removing a lot of things is still slower for Linked lists than arrays since you have a lot of syscalls (which is pretty slow overall)
And python decided to use Linked lists instead of arrays 🤡
@@TurdBoi-tf5lf oof, I was sure I elaborated already. Anywhays, Arrays use less memory, are faster to exoand and even faster to iterate over. As a matter off acts, iterating over an array of less than 5k elements in C is generally faster than using a hashmap. The memory is self explanatory, the array has a fat pointer to the start (or normal pointer) and then you go trough it. The Linked list has generally around double the memory consumption (pointer to next address which generally is 64-bit number, and the value (often also a pointer). Now surprisingly normal arrays are often faster when expanding as well because you only have 1 (or maybe 2) system calls for expanding (normally doubling) the content in the array. For linked lists you have a syscall per item. So lists are slower add a lot of data, they are slower to iterate over, have no random access and consume around half the memory.
This is alos the reason why pretty much every langiage that takes performance even remotely seriously implements lists as ArrayLists (Dynamic Arrays, vectors, call them how you like), and not as LinkedLists. Also, most algorithms on linked lists are significantly slower than arrays. Small bonus. Iteration over arrays is so fast because your CPU doesn't just load the data it needs from the RAM into it's cache, but it loads memory blocks. When it loads a memory block of array data, it automatically loads quite a few elements of the array, and therefore has more cache hits. LinkedLists are across the heap, therefore cache hits are honestly more the exception than the rule
Shout out to all the homies nodding along with NO DOGGARN CLUE WTF IS GOING ON... I'm here with you bro
Like... I know about arrays and lists but for others... I don't know what the F is going on
Nice explanation, but the heap comes a little short here ;)
As a JS, TS and Go Web Developer... I am yet to find the usage of most of these data structures. I have used arrays, list, hash tables or tables.. but apart from these ones, I haven't seen the usage of others.
Heaps can be quite valuable in e-commerce for tracking the top k most sold items. This is a useful metric for identifying where to focus your efforts and also helps in creating bestseller lists or targeted promotions for customers.
What about the DOM, that's a tree data structure with references to it in CSS with selectors. If you make a chat function with a queue with a fifo buffer for retry/ack functionality etc. Sometimes you may need to use open street maps, the geo data it contains is not always key/value, it's also a tree of json data that needs to be traversed. You can do that recursively, then you need to know about the underlaying programming language stack handling. If the recursion goes too deep you run into issues.
JS and other language and frameworks tend to hide all that but it's good to know that they're there and you're actually using them.
Except Java, it's all very good content 😉
* looks at PC *
Tell me you have small children without telling me
So what is a JavaScript array? Is it like a doubly linked list that contains pointers to malloced chunks of memory and a size of the malloced element?
“Stuff you should know if you want to get hired, but then never use because there’s libraries for all this”.
I liked this video a lot, really does remind me of my first year in CS
Hi, can I please get a link for your keyboard? It looks awesome and I wanna buy one, thanks.
I really think we should be cautious about "things that every programmer should know".
In a computer science cursus you learn lots of concepts and stuffs, but it depends on what branche you'll be working in after graduating. For instance as a web dev, you'll mostly only work with an array.
If you do work with lower level langages like C, then you might use more sophisticated data structures to use less memory or less cpu.
Si to people desiring to switch job and start programming, Please ask round you what data structures are really used
And save your time.
Amazing video. Does any know what type of font he use?
bro im a nurse teaching myself how to code and this is excellent stuff thank you!
This video is great and makes these topics easy to understand, thank you so much Forrest!
Valuable content
first!
first
Noted....2nd
Nice
Nice
Uh the good old c-queue that is actually an array and goes lightning fast, if you only use bit manipulation, because your CPU does a quadrillion of those before it read in a line of RAM, completely obliterating any O() notation when you measure the actuall execution time in many cases.
Great video! Thank you sir !
The one point I disagree with is about the definition of an array. Same type for each slot and contiguous memory storage, yes, but not statically sized. And there's a lot more complexity to hash tables if you care to make a more complete description there. You have different hashing algorithms, balancing schemes, and even storage methods. For instance, in one implementation, I made the storage backing for a hash table as a balanced tree and each node was also part of an interlaced series of trees that were the chains. It used up a lot more memory than either a tree or a hash table alone, but it was an interesting experiment in maintaining sorted order while yielding slightly faster searches.
can you tell me your keyboard and model name please
your keyboard name is.?
Great explanation
Super video.
Thanks
I was wondering if you may go deeper in another video series for each data structure with a coding example on how to build them and where we can find them in real world applications?
True golden nugget. In 17 minutes, you simplified what my professor couldn't in hours of lectures!!!
Dude stop
Don't be trapped. Paid Programming is moving to India. Get a different trade folks
What u mean?
He is brainwashing you@@Homelander_30
Lmfao 😂
Super power saar
I'm a dev from india and this is true. During the recent layoffs they cleared out all of US/UK offices except the sales folks. Now that the feds have slashed interest rates they have started hiring here in the Bangalore office
10:14 - url for that chart?