title

Learn Data Structures and Algorithms for free π

description

Data Structures and Algorithms full course tutorial java
#data #structures #algorithms
βοΈTime StampsβοΈ
#1 (00:00:00) What are data structures and algorithms? π
#2 (00:02:20) Stacks π
#3 (00:11:45) Queues ποΈ
#4 (00:21:51) Priority Queues π₯
#5 (00:26:51) Linked Lists π
#6 (00:40:14) Dynamic Arrays π±
#7 (01:04:37) LinkedLists vs ArrayLists π€ΌββοΈ
#8 (01:13:07) Big O notation π
#9 (01:19:32) Linear search β¬οΈ
#10 (01:23:13) Binary search πͺ
#11 (01:32:44) Interpolation search β
#12 (01:41:05) Bubble sort π€Ώ
#13 (01:48:14) Selection sort π¦
#14 (01:56:35) Insertion sort π§©
#15 (02:03:40) Recursion π΅
#16 (02:11:58) Merge sort πͺ
#17 (02:25:07) Quick sort β‘
#18 (02:38:57) Hash Tables #οΈβ£
#19 (02:52:21) Graphs intro π
#20 (02:57:39) Adjacency matrix β¬
#21 (03:07:30) Adjacency list π
#22 (03:15:59) Depth First Search β¬οΈ
#23 (03:23:40) Breadth First Search βοΈ
#24 (03:30:20) Tree data structure intro π³
#25 (03:33:14) Binary search tree π
#26 (03:53:38) Tree traversal π§
#27 (03:57:35) Calculate execution time β±οΈ
Code from each video can be found pinned in the comments section of the original series playlist
music credits πΌ :
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Up In My Jam (All Of A Sudden) by - Kubbi https://soundcloud.com/kubbi
Creative Commons β Attribution-ShareAlike 3.0 Unportedβ CC BY-SA 3.0
Free Download / Stream: http://bit.ly/2JnDfCE
Music promoted by Audio Library https://youtu.be/tDexBj46oNI
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Twelve Speed (Alone Time Vol. 2) by - Slynk
Link - https://youtu.be/jsTEUi-kegE
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YouTube Audio Library Vol. 3 by - Bad Snacks
Link - https://youtu.be/WyOdBcADtp8
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Copyright Disclaimer:
This video is the intellectual property of Bro Code. All rights reserved. No part of this video may be reproduced, distributed, or transmitted in any form or by any means, including but not limited to recording, uploading, or other electronic or mechanical methods, without my written permission, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law.

detail

{'title': 'Learn Data Structures and Algorithms for free π', 'heatmap': [{'end': 2883.925, 'start': 2733.826, 'weight': 0.754}, {'end': 7067.339, 'start': 6914.784, 'weight': 1}, {'end': 8939.366, 'start': 8650.356, 'weight': 0.776}, {'end': 9518.965, 'start': 9368.041, 'weight': 0.705}, {'end': 11102.657, 'start': 10952.611, 'weight': 0.712}], 'summary': 'Series covers data structures and algorithms, including stack, queue, linked list, dynamic arrays, big o notation, search algorithms, sorting algorithms, hash tables, graphs, adjacency representation, dfs, bfs, and binary search trees, with detailed explanations, examples, and practical applications, aiding in coding job interviews and offering a 50% success rate for binary search tree methods.', 'chapters': [{'end': 665.663, 'segs': [{'end': 46.585, 'src': 'embed', 'start': 22.207, 'weight': 3, 'content': [{'end': 31.835, 'text': 'First, we will learn about basic data structures, followed by big O notation, then searching algorithms, sorting algorithms, graphs, and trees.', 'start': 22.207, 'duration': 9.628}, {'end': 35.138, 'text': "Be sure to look for the timestamps in this video's description.", 'start': 32.316, 'duration': 2.822}, {'end': 43.743, 'text': "Well, what's a data structure? Simply put, a data structure is a named location that can be used to store and organize data.", 'start': 35.819, 'duration': 7.924}, {'end': 45.184, 'text': "Here's a real life example.", 'start': 43.983, 'duration': 1.201}, {'end': 46.585, 'text': 'Think of a family tree.', 'start': 45.244, 'duration': 1.341}], 'summary': 'Introduction to data structures including basic concepts and examples.', 'duration': 24.378, 'max_score': 22.207, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s22207.jpg'}, {'end': 139.935, 'src': 'embed', 'start': 112.909, 'weight': 0, 'content': [{'end': 116.049, 'text': 'A linear search is an example of a searching algorithm.', 'start': 112.909, 'duration': 3.14}, {'end': 117.57, 'text': "I know what you're probably thinking.", 'start': 116.389, 'duration': 1.181}, {'end': 121.751, 'text': "Why do I need to learn data structures and algorithms? What's the point? Well, let me tell you.", 'start': 117.79, 'duration': 3.961}, {'end': 125.092, 'text': "You'll write code that is both time and memory efficient.", 'start': 122.111, 'duration': 2.981}, {'end': 131.133, 'text': 'And two, commonly asked questions involve data structures and algorithms in coding job interviews.', 'start': 125.592, 'duration': 5.541}, {'end': 136.915, 'text': "Do you know how to reverse a doubly linked list? No, no you don't, do you? So you should probably watch this series.", 'start': 131.373, 'duration': 5.542}, {'end': 139.935, 'text': "So without further ado, ladies and gentlemen, let's begin.", 'start': 137.235, 'duration': 2.7}], 'summary': 'Learning data structures and algorithms leads to efficient code and prepares for coding job interviews.', 'duration': 27.026, 'max_score': 112.909, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s112909.jpg'}, {'end': 203.89, 'src': 'embed', 'start': 154.705, 'weight': 2, 'content': [{'end': 157.285, 'text': 'A stack is a LIFO data structure.', 'start': 154.705, 'duration': 2.58}, {'end': 160.847, 'text': 'LIFO is an acronym for Last In First Out.', 'start': 157.466, 'duration': 3.381}, {'end': 167.809, 'text': 'This allows us to store objects into a sort of vertical tower like a stack of books or a stack of CDs.', 'start': 161.287, 'duration': 6.522}, {'end': 175.431, 'text': 'We use the push method to add objects to the top of the stack and the pop method to remove objects from the top.', 'start': 168.349, 'duration': 7.082}, {'end': 179.973, 'text': "So here's a real-life example of us creating a stack data structure.", 'start': 175.892, 'duration': 4.081}, {'end': 183.915, 'text': 'After declaring our stack, we can add objects to our stack.', 'start': 180.393, 'duration': 3.522}, {'end': 186.857, 'text': "The objects that we'll be adding are various video games.", 'start': 183.996, 'duration': 2.861}, {'end': 192.701, 'text': "To add an object to our stack, we use the push method, like we're pushing something onto our stack.", 'start': 187.318, 'duration': 5.383}, {'end': 196.024, 'text': "So let's push Minecraft onto the bottom of our stack.", 'start': 193.042, 'duration': 2.982}, {'end': 198.585, 'text': "And then next, we'll add Skyrim.", 'start': 196.584, 'duration': 2.001}, {'end': 200.126, 'text': 'Then maybe some Doom.', 'start': 199.106, 'duration': 1.02}, {'end': 203.89, 'text': 'then Borderlands, and then Final Fantasy VII.', 'start': 201.187, 'duration': 2.703}], 'summary': 'A stack is a lifo data structure for storing objects using push and pop methods. real-life example: adding video games to a stack.', 'duration': 49.185, 'max_score': 154.705, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s154705.jpg'}, {'end': 647.533, 'src': 'embed', 'start': 617.999, 'weight': 1, 'content': [{'end': 621.302, 'text': 'Now this is an interesting feature that may or may not happen to you.', 'start': 617.999, 'duration': 3.303}, {'end': 638.871, 'text': "If you add a billion copies of Skyrim onto a stack, Dammit, Todd Howard, you did it again! Now, what are some uses of stacks? Here's a few examples.", 'start': 621.663, 'duration': 17.208}, {'end': 647.533, 'text': 'One, we can use them in undo, redo features in text editors, like when we go to edit, then undo typing, or redo typing.', 'start': 639.151, 'duration': 8.382}], 'summary': 'Using stacks can aid in undo and redo features in text editors, such as in the case of adding a billion copies of skyrim onto a stack.', 'duration': 29.534, 'max_score': 617.999, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s617999.jpg'}], 'start': 0.269, 'title': 'Data structures & algorithms', 'summary': 'Introduces data structures and algorithms, explaining their purpose, providing real-life examples, and highlighting their relevance to coding job interviews. it also explores the stack data structure, its lifo nature, methods, practical applications, using java as an example.', 'chapters': [{'end': 131.133, 'start': 0.269, 'title': 'Data structures & algorithms', 'summary': 'Introduces data structures and algorithms, explaining the purpose of data structures and algorithms, providing real-life examples, and highlighting the importance of learning them for time and memory efficiency, as well as their relevance to coding job interviews.', 'duration': 130.864, 'highlights': ['The chapter explains the purpose of data structures and algorithms, providing real-life examples like a family tree and arrays to illustrate the concept of data structures.', 'It introduces the concept of algorithms as a collection of steps to solve a problem, providing a real-life example of baking a pizza and explaining how it correlates to an algorithm.', 'The chapter emphasizes the importance of learning data structures and algorithms for writing time and memory-efficient code, as well as their relevance to coding job interviews.']}, {'end': 665.663, 'start': 131.373, 'title': 'Exploring stack data structure', 'summary': 'Introduces the stack data structure, explaining its lifo nature, methods, and practical applications, using java as an example.', 'duration': 534.29, 'highlights': ['The stack is a LIFO data structure, allowing objects to be stored and retrieved in a Last In First Out manner. It explains the LIFO nature of a stack and how objects are stored and retrieved.', 'The push method adds objects to the top of the stack, while the pop method removes objects from the top. Explains the push and pop methods, which add and remove objects from the stack.', 'The chapter demonstrates the implementation of a stack in Java, showcasing the declaration, instantiation, and methods of the stack data structure. Demonstrates the implementation of a stack in Java, including its declaration, instantiation, and methods.', 'Practical applications of stacks include undo/redo features in text editors, browser history navigation, backtracking algorithms, and function calls. It provides practical applications of stacks such as in text editors, browser history navigation, backtracking algorithms, and function calls.', 'A for loop example highlights the possibility of running out of memory when adding a large number of objects to a stack. Demonstrates the possibility of running out of memory when adding a large number of objects to a stack.']}], 'duration': 665.394, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s269.jpg', 'highlights': ['The chapter emphasizes the importance of learning data structures and algorithms for writing time and memory-efficient code, as well as their relevance to coding job interviews.', 'Practical applications of stacks include undo/redo features in text editors, browser history navigation, backtracking algorithms, and function calls.', 'The push method adds objects to the top of the stack, while the pop method removes objects from the top.', 'The chapter explains the purpose of data structures and algorithms, providing real-life examples like a family tree and arrays to illustrate the concept of data structures.', 'The stack is a LIFO data structure, allowing objects to be stored and retrieved in a Last In First Out manner.']}, {'end': 1541.994, 'segs': [{'end': 691.161, 'src': 'embed', 'start': 665.823, 'weight': 0, 'content': [{'end': 673.108, 'text': "Whenever we call a function, we add what is known as a frame to the call stack, but I'll probably save this topic for another video.", 'start': 665.823, 'duration': 7.285}, {'end': 677.911, 'text': 'So in conclusion everybody, a stack is a LIFO data structure.', 'start': 673.388, 'duration': 4.523}, {'end': 679.553, 'text': 'Last in, first out.', 'start': 678.151, 'duration': 1.402}, {'end': 684.456, 'text': 'It stores objects into a sort of vertical tower, a stack of objects.', 'start': 679.893, 'duration': 4.563}, {'end': 691.161, 'text': 'You use the push method to add objects to the top, and the pop method to remove objects from the top.', 'start': 684.836, 'duration': 6.325}], 'summary': 'A stack is a lifo data structure storing objects in a vertical tower.', 'duration': 25.338, 'max_score': 665.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s665823.jpg'}, {'end': 836.966, 'src': 'embed', 'start': 806.141, 'weight': 2, 'content': [{'end': 807.483, 'text': "So that's kind of how a queue works.", 'start': 806.141, 'duration': 1.342}, {'end': 809.064, 'text': "It's first come, first serve.", 'start': 807.643, 'duration': 1.421}, {'end': 810.986, 'text': 'FIFO First in, first out.', 'start': 809.244, 'duration': 1.742}, {'end': 813.489, 'text': 'If you were here first, then you will be helped first.', 'start': 811.306, 'duration': 2.183}, {'end': 820.015, 'text': 'Now the concepts of adding and removing objects from a queue are known as both enqueuing and dequeuing.', 'start': 813.909, 'duration': 6.106}, {'end': 825.259, 'text': 'Enqueuing is the concept of adding an object to the end of our queue, our tail.', 'start': 820.435, 'duration': 4.824}, {'end': 829.682, 'text': 'And dequeuing is when we remove an object from the head of our queue.', 'start': 825.78, 'duration': 3.902}, {'end': 836.966, 'text': "Now, what methods do you need to enqueue and dequeue from a queue? Well, it really depends on the programming language that you're working with.", 'start': 830.102, 'duration': 6.864}], 'summary': 'Queues operate on fifo principle for adding/removing objects. enqueuing adds to end, dequeuing removes from head.', 'duration': 30.825, 'max_score': 806.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s806141.jpg'}, {'end': 979.364, 'src': 'embed', 'start': 954.292, 'weight': 3, 'content': [{'end': 960.915, 'text': "However, they throw exceptions and according to the documentation, it's actually better to use this column of methods instead.", 'start': 954.292, 'duration': 6.623}, {'end': 962.856, 'text': 'These will return a special value.', 'start': 961.255, 'duration': 1.601}, {'end': 968.779, 'text': 'We have offer, which will enqueue or add an element to the tail of our queue.', 'start': 963.276, 'duration': 5.503}, {'end': 973.661, 'text': 'poll will dequeue, it will remove the head of our current queue.', 'start': 969.339, 'duration': 4.322}, {'end': 979.364, 'text': 'And then there is also peek, it will not remove the head, but it will examine it and return it.', 'start': 974.101, 'duration': 5.263}], 'summary': 'Use offer to enqueue, poll to dequeue, and peek to examine the head of the queue.', 'duration': 25.072, 'max_score': 954.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s954292.jpg'}, {'end': 1347.516, 'src': 'embed', 'start': 1319.515, 'weight': 4, 'content': [{'end': 1322.237, 'text': 'So sit back, relax, and enjoy the show.', 'start': 1319.515, 'duration': 2.722}, {'end': 1328.006, 'text': "Hey, what's going on everybody? So priority queues.", 'start': 1324.844, 'duration': 3.162}, {'end': 1331.648, 'text': 'A priority queue is a FIFO data structure.', 'start': 1328.226, 'duration': 3.422}, {'end': 1333.109, 'text': 'First in, first out.', 'start': 1331.788, 'duration': 1.321}, {'end': 1340.933, 'text': 'However, a major difference with priority queues is that before we start polling and serving elements, we put them in some sort of order.', 'start': 1333.469, 'duration': 7.464}, {'end': 1346.256, 'text': 'Higher priority elements are served first before elements with lower priority.', 'start': 1341.373, 'duration': 4.883}, {'end': 1347.516, 'text': "So here's an example.", 'start': 1346.596, 'duration': 0.92}], 'summary': 'Priority queues serve higher priority elements first in a fifo data structure.', 'duration': 28.001, 'max_score': 1319.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s1319515.jpg'}], 'start': 665.823, 'title': 'Understanding stack and fifo queue data structures', 'summary': 'Explains stack data structures as a last in, first out (lifo) data structure used in computer science, and fifo queue data structures as first in, first out (fifo) data structures with examples of adding and removing elements including the methods used, and introduces the concept of priority queues.', 'chapters': [{'end': 745.614, 'start': 665.823, 'title': 'Understanding stack data structures', 'summary': 'Explains stack data structures as a last in, first out (lifo) data structure used in computer science, where objects are stored in a vertical tower and added using the push method and removed using the pop method.', 'duration': 79.791, 'highlights': ['A stack is a LIFO data structure, storing objects in a vertical tower. Explains that a stack is a Last In, First Out (LIFO) data structure.', 'The push method is used to add objects to the top of the stack, and the pop method is used to remove objects from the top. Describes the methods used to add and remove objects from a stack.', 'In the next video, the topic of queues will be covered. Mentions the topic of queues for the next video.']}, {'end': 1541.994, 'start': 745.874, 'title': 'Understanding fifo queue data structure', 'summary': 'Explains the first in, first out (fifo) queue data structure with examples of adding and removing elements, using the offer and poll methods, and introduces the concept of priority queues with examples of arranging elements based on priority.', 'duration': 796.12, 'highlights': ['The chapter explains the First In, First Out (FIFO) queue data structure It provides a detailed explanation of how a queue works, emphasizing the FIFO principle of servicing customers in the order they arrived, and introduces the concepts of enqueuing and dequeuing.', 'Examples of adding and removing elements, using the offer and poll methods The chapter demonstrates adding elements to a queue using the offer method and removing elements using the poll method, providing a practical demonstration of how queues function.', 'Introduction of priority queues with examples of arranging elements based on priority It introduces the concept of priority queues, highlighting the difference from standard queues by arranging elements based on priority before servicing, and provides examples of arranging student GPAs in ascending order based on priority.']}], 'duration': 876.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s665823.jpg', 'highlights': ['A stack is a Last In, First Out (LIFO) data structure, storing objects in a vertical tower.', 'The push method adds objects to the top of the stack, and the pop method removes objects from the top.', 'The chapter explains the First In, First Out (FIFO) queue data structure and introduces the concepts of enqueuing and dequeuing.', 'Examples of adding and removing elements are demonstrated using the offer and poll methods.', 'Introduction of priority queues with examples of arranging elements based on priority.']}, {'end': 2403.42, 'segs': [{'end': 1593.861, 'src': 'embed', 'start': 1542.074, 'weight': 0, 'content': [{'end': 1544.134, 'text': "Let's say that these are now strings.", 'start': 1542.074, 'duration': 2.06}, {'end': 1547.055, 'text': "And let's put these in standard order.", 'start': 1544.754, 'duration': 2.301}, {'end': 1549.475, 'text': "So let's change these to strings.", 'start': 1547.555, 'duration': 1.92}, {'end': 1551.176, 'text': 'Maybe this student has a B.', 'start': 1549.775, 'duration': 1.401}, {'end': 1562.589, 'text': 'This one has a C, A, F, and what are we missing? D.', 'start': 1552.622, 'duration': 9.967}, {'end': 1567.693, 'text': 'If we have a priority queue of strings, well then these elements will be in alphabetical order.', 'start': 1562.589, 'duration': 5.104}, {'end': 1576.179, 'text': 'So if we need these in reverse alphabetical order, again we can pass in a comparator, and then one that we can use is from collections.', 'start': 1568.333, 'duration': 7.846}, {'end': 1586.035, 'text': 'Collections.reverseOrder And these elements within our priority queue are now within reverse alphabetical order.', 'start': 1576.639, 'duration': 9.396}, {'end': 1588.237, 'text': "So yeah, that's a priority queue.", 'start': 1586.636, 'duration': 1.601}, {'end': 1589.658, 'text': 'Think of it like a queue.', 'start': 1588.657, 'duration': 1.001}, {'end': 1593.861, 'text': 'However, we first sort these elements based on a certain priority.', 'start': 1589.998, 'duration': 3.863}], 'summary': 'Using a priority queue of strings, elements can be sorted and reversed alphabetically.', 'duration': 51.787, 'max_score': 1542.074, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s1542074.jpg'}, {'end': 1883.645, 'src': 'embed', 'start': 1855.017, 'weight': 6, 'content': [{'end': 1858.178, 'text': 'This variation of a linked list is a singly linked list.', 'start': 1855.017, 'duration': 3.161}, {'end': 1860.419, 'text': 'There are single links to each node.', 'start': 1858.378, 'duration': 2.041}, {'end': 1864.24, 'text': "However, there's another variation called a doubly linked list.", 'start': 1860.659, 'duration': 3.581}, {'end': 1871.942, 'text': 'A doubly linked list requires even more memory to store two addresses in each node, not just one, which is the case with a singly linked list.', 'start': 1864.58, 'duration': 7.362}, {'end': 1875.843, 'text': 'One address for the next node and another for the previous node in our chain.', 'start': 1872.382, 'duration': 3.461}, {'end': 1883.645, 'text': 'The benefit of a doubly linked list is that we can traverse our doubly linked list from head to tail or from tail to head in reverse.', 'start': 1876.223, 'duration': 7.422}], 'summary': 'Singly linked list has single links to each node, while doubly linked list requires more memory for two addresses per node.', 'duration': 28.628, 'max_score': 1855.017, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s1855017.jpg'}, {'end': 2333.766, 'src': 'embed', 'start': 2304.005, 'weight': 1, 'content': [{'end': 2306.947, 'text': 'Two, insertion and deletion of nodes is really easy.', 'start': 2304.005, 'duration': 2.942}, {'end': 2310.631, 'text': "If you're familiar with Big O notation, this would be in constant time.", 'start': 2307.308, 'duration': 3.323}, {'end': 2313.834, 'text': "There's only a few steps regardless of the size of our data set.", 'start': 2310.851, 'duration': 2.983}, {'end': 2316.677, 'text': 'And three, there is no too low memory waste.', 'start': 2314.194, 'duration': 2.483}, {'end': 2323.18, 'text': 'What are some disadvantages? One, there is greater memory usage because we have to store an additional pointer.', 'start': 2317.117, 'duration': 6.063}, {'end': 2329.944, 'text': 'Each node also stores the address for where the next node is located, and even more so, with a doubly linked list.', 'start': 2323.52, 'duration': 6.424}, {'end': 2333.766, 'text': 'this will use a lot more memory, because we need two addresses for each node.', 'start': 2329.944, 'duration': 3.822}], 'summary': 'Linked lists allow easy insertion/deletion in constant time, with some disadvantages like increased memory usage due to additional pointers.', 'duration': 29.761, 'max_score': 2304.005, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2304005.jpg'}, {'end': 2386.53, 'src': 'embed', 'start': 2343.914, 'weight': 3, 'content': [{'end': 2347.797, 'text': 'And three, accessing and searching of elements is more time consuming.', 'start': 2343.914, 'duration': 3.883}, {'end': 2349.799, 'text': 'This is done in linear time.', 'start': 2348.118, 'duration': 1.681}, {'end': 2352.902, 'text': 'This is where arrays and array lists have an advantage.', 'start': 2350.179, 'duration': 2.723}, {'end': 2357.966, 'text': 'Since they use indexing, they can randomly access an element of an array or an array list.', 'start': 2353.182, 'duration': 4.784}, {'end': 2365.832, 'text': "With a linked list, we have to manually traverse the entire linked list to get to a particular index, since we don't have random access.", 'start': 2358.366, 'duration': 7.466}, {'end': 2370.216, 'text': 'Now, what are some uses of linked lists? One, they could implement stacks or queues.', 'start': 2366.393, 'duration': 3.823}, {'end': 2373.719, 'text': 'If you need a stack or queue for anything, you could also use a linked list.', 'start': 2370.296, 'duration': 3.423}, {'end': 2375.861, 'text': 'Two, maybe GPS navigation.', 'start': 2374.099, 'duration': 1.762}, {'end': 2379.424, 'text': "So let's say you have a starting position and a final destination.", 'start': 2376.321, 'duration': 3.103}, {'end': 2382.426, 'text': 'Each step or stop along the way is kind of like a node.', 'start': 2379.844, 'duration': 2.582}, {'end': 2386.53, 'text': 'And if you need to take a detour, you can easily change, insert, or delete a node.', 'start': 2382.747, 'duration': 3.783}], 'summary': 'Linked lists have advantages in implementing stacks or queues and in gps navigation.', 'duration': 42.616, 'max_score': 2343.914, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2343914.jpg'}], 'start': 1542.074, 'title': 'Priority queues and linked lists in computer science', 'summary': 'Covers priority queues, demonstrating sorting based on priority and efficient element retrieval. it also discusses linked lists, highlighting advantages, disadvantages, use cases, and differences between singly and doubly linked lists.', 'chapters': [{'end': 1615.717, 'start': 1542.074, 'title': 'Priority queues in computer science', 'summary': 'Explains the concept of priority queues, demonstrating how elements are sorted based on priority using alphabetical and reverse alphabetical order, and the efficient retrieval of elements based on priority.', 'duration': 73.643, 'highlights': ['The elements in a priority queue are sorted based on priority, allowing for efficient retrieval of elements with the highest priorities first.', "Demonstrates sorting elements in alphabetical order within a priority queue, such as 'A', 'B', 'C', 'D', 'F'.", "Explains the process of sorting elements in reverse alphabetical order within a priority queue using a comparator from collections, such as 'F', 'D', 'C', 'B', 'A'."]}, {'end': 2403.42, 'start': 1615.957, 'title': 'Linked lists in computer science', 'summary': 'Discusses the advantages and disadvantages of linked lists compared to arrays and array lists, highlighting the ease of insertion and deletion of nodes, the inefficiency in searching and accessing elements, and the various use cases of linked lists such as implementing stacks or queues, gps navigation, and music playlists. it also explains the differences between singly linked lists and doubly linked lists, stating that linked lists are dynamic data structures and can allocate needed memory while the program is running.', 'duration': 787.463, 'highlights': ['Linked lists excel at insertion and deletion of nodes, as it only requires a few steps regardless of the data set size, making it a constant time operation Insertion and deletion of nodes in linked lists are easy and efficient, with a constant time complexity, regardless of the size of the data set.', 'Linked lists lack random access to elements, leading to more time-consuming linear time searching and accessing of elements, unlike arrays and array lists which can use indexing to randomly access elements Unlike arrays and array lists, linked lists lack random access to elements, resulting in linear time complexity for searching and accessing elements.', 'Singly linked lists have single links to each node, while doubly linked lists have two addresses in each node, allowing traversal from head to tail or tail to head, but requiring more memory Singly linked lists have single links to each node, while doubly linked lists have two addresses in each node, enabling traversal from head to tail or tail to head, but leading to higher memory usage.', 'Linked lists can be used to implement stacks or queues, GPS navigation, and music playlists due to their dynamic nature and ease of insertion and deletion of nodes Linked lists find applications in implementing stacks or queues, GPS navigation, and music playlists, leveraging their dynamic nature and ease of insertion and deletion of nodes.']}], 'duration': 861.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s1542074.jpg', 'highlights': ['The elements in a priority queue are sorted based on priority, allowing for efficient retrieval of elements with the highest priorities first.', 'Linked lists excel at insertion and deletion of nodes, as it only requires a few steps regardless of the data set size, making it a constant time operation.', "Demonstrates sorting elements in alphabetical order within a priority queue, such as 'A', 'B', 'C', 'D', 'F'.", 'Linked lists can be used to implement stacks or queues, GPS navigation, and music playlists due to their dynamic nature and ease of insertion and deletion of nodes.', "Explains the process of sorting elements in reverse alphabetical order within a priority queue using a comparator from collections, such as 'F', 'D', 'C', 'B', 'A'.", 'Linked lists lack random access to elements, leading to more time-consuming linear time searching and accessing of elements, unlike arrays and array lists which can use indexing to randomly access elements.', 'Singly linked lists have single links to each node, while doubly linked lists have two addresses in each node, allowing traversal from head to tail or tail to head, but requiring more memory.']}, {'end': 4378.852, 'segs': [{'end': 2447.724, 'src': 'embed', 'start': 2422.574, 'weight': 0, 'content': [{'end': 2429.976, 'text': 'if we need extra room for elements, we can increase the capacity which we cannot normally do with a standard, typical fixed size array.', 'start': 2422.574, 'duration': 7.402}, {'end': 2437.658, 'text': 'dynamic arrays are also known as array lists in java, vectors in c plus plus arrays in javascript and list in python.', 'start': 2429.976, 'duration': 7.682}, {'end': 2441.5, 'text': "Here's an example of a static array, and then we'll take a look at a dynamic array.", 'start': 2437.918, 'duration': 3.582}, {'end': 2443.841, 'text': 'A static array has a fixed capacity.', 'start': 2441.74, 'duration': 2.101}, {'end': 2447.724, 'text': "We determine that capacity at compile time, and we can't change it later normally.", 'start': 2444.102, 'duration': 3.622}], 'summary': 'Dynamic arrays allow for increased capacity, unlike static arrays.', 'duration': 25.15, 'max_score': 2422.574, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2422574.jpg'}, {'end': 2599.748, 'src': 'embed', 'start': 2567.065, 'weight': 2, 'content': [{'end': 2567.825, 'text': 'You do you, I guess.', 'start': 2567.065, 'duration': 0.76}, {'end': 2571.926, 'text': 'A dynamic array has its own inner static array with a fixed size.', 'start': 2568.165, 'duration': 3.761}, {'end': 2576.768, 'text': 'Once the inner static array of our dynamic array reaches capacity,', 'start': 2572.366, 'duration': 4.402}, {'end': 2581.689, 'text': 'our dynamic array will declare and instantiate a new array with an increased capacity.', 'start': 2576.768, 'duration': 4.921}, {'end': 2586.811, 'text': 'Usually the amount that we increase the capacity by really varies depending on the programming language.', 'start': 2582.17, 'duration': 4.641}, {'end': 2589.554, 'text': "It's usually between 1.5 and 2.", 'start': 2587.151, 'duration': 2.403}, {'end': 2592.138, 'text': 'I just picked capacity times 2 for extra emphasis.', 'start': 2589.554, 'duration': 2.584}, {'end': 2599.748, 'text': "So what we'll do now is copy the elements over to our new array, and these have different memory addresses than our original array.", 'start': 2592.558, 'duration': 7.19}], 'summary': 'Dynamic array increases capacity by 1.5-2x, copying elements to new array.', 'duration': 32.683, 'max_score': 2567.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2567065.jpg'}, {'end': 2658.342, 'src': 'embed', 'start': 2633.381, 'weight': 1, 'content': [{'end': 2640.067, 'text': 'So you just shift all the elements to the right by one to insert a new element or shift all the elements to the left to delete an element.', 'start': 2633.381, 'duration': 6.686}, {'end': 2647.233, 'text': 'What are some of the advantages of dynamic arrays? One, there is random access of elements that is done in O of one constant time.', 'start': 2640.447, 'duration': 6.786}, {'end': 2651.216, 'text': 'We can randomly access an element by an index number and retrieve the value.', 'start': 2647.513, 'duration': 3.703}, {'end': 2658.342, 'text': 'Two, there is good locality of reference and data cache utilization because all of these memory addresses are contiguous.', 'start': 2651.616, 'duration': 6.726}], 'summary': 'Dynamic arrays allow random access in o(1) time and utilize contiguous memory for good data cache utilization.', 'duration': 24.961, 'max_score': 2633.381, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2633381.jpg'}, {'end': 2883.925, 'src': 'heatmap', 'start': 2733.826, 'weight': 0.754, 'content': [{'end': 2740.191, 'text': "I thought we would create our own dynamic array just for learning purposes and practice, but let's take a look at the ArrayList class.", 'start': 2733.826, 'duration': 6.365}, {'end': 2743.093, 'text': 'Within this class, there are a few defined members.', 'start': 2740.671, 'duration': 2.422}, {'end': 2745.916, 'text': 'There is a default capacity set to 10.', 'start': 2743.474, 'duration': 2.442}, {'end': 2749.138, 'text': 'There are overloaded constructors within this ArrayList class.', 'start': 2745.916, 'duration': 3.222}, {'end': 2755.143, 'text': 'We can set our own initial capacity, or we can use the default by not passing in an initial capacity.', 'start': 2749.458, 'duration': 5.685}, {'end': 2760.886, 'text': 'There is a size to keep track of how many elements are filled within our array list,', 'start': 2755.663, 'duration': 5.223}, {'end': 2765.408, 'text': 'and our array list does have its own inner static fixed size array.', 'start': 2760.886, 'duration': 4.522}, {'end': 2771.452, 'text': 'And if we ever need to expand the size of this array, we just copy the elements over to a new inner array.', 'start': 2765.829, 'duration': 5.623}, {'end': 2772.692, 'text': "So let's begin.", 'start': 2771.992, 'duration': 0.7}, {'end': 2776.714, 'text': "Let's create a new class named dynamic array, and I'll get rid of this.", 'start': 2772.852, 'duration': 3.862}, {'end': 2782.818, 'text': 'So file, new, class, and this will be named dynamic array.', 'start': 2777.235, 'duration': 5.583}, {'end': 2784.426, 'text': 'Then finish.', 'start': 2783.806, 'duration': 0.62}, {'end': 2786.668, 'text': "Okay, let's declare a few members.", 'start': 2785.127, 'duration': 1.541}, {'end': 2790.63, 'text': "Let's create int size, int capacity.", 'start': 2786.848, 'duration': 3.782}, {'end': 2792.311, 'text': 'This will be the initial capacity.', 'start': 2790.79, 'duration': 1.521}, {'end': 2799.115, 'text': "I'll set this to 10, but feel free to pick whatever value that you want, as well as an array of objects named array.", 'start': 2792.671, 'duration': 6.444}, {'end': 2801.856, 'text': 'I will declare this, but not yet instantiate it.', 'start': 2799.515, 'duration': 2.341}, {'end': 2803.937, 'text': 'So you can make these private.', 'start': 2802.317, 'duration': 1.62}, {'end': 2809.621, 'text': "However, I think that'll make our code a little more complex and difficult to understand, although it'd be more secure.", 'start': 2804.178, 'duration': 5.443}, {'end': 2813.223, 'text': "I'm just going to use the default visibility for these members here.", 'start': 2810.001, 'duration': 3.222}, {'end': 2816.687, 'text': "all right, let's create some overloaded constructors.", 'start': 2813.843, 'duration': 2.844}, {'end': 2826.4, 'text': 'so public dynamic array, and within here we will instantiate a new fixed size array.', 'start': 2816.687, 'duration': 9.713}, {'end': 2836.999, 'text': 'this dot array equals new array of objects with a capacity of whatever capacity the default is.', 'start': 2826.4, 'duration': 10.599}, {'end': 2845.841, 'text': "so it's going to be 10 by default and we'll create an overloaded constructor just in case the user passes in their own capacity that they would like to set.", 'start': 2836.999, 'duration': 8.842}, {'end': 2847.961, 'text': 'so int capacity.', 'start': 2845.841, 'duration': 2.12}, {'end': 2854.382, 'text': 'this dot capacity equals whatever capacity that we pass in.', 'start': 2847.961, 'duration': 6.421}, {'end': 2858.483, 'text': "okay, let's instantiate a new dynamic array.", 'start': 2854.382, 'duration': 4.101}, {'end': 2861.35, 'text': 'dynamic, Make sure to spell it right.', 'start': 2858.483, 'duration': 2.867}, {'end': 2862.711, 'text': 'Dynamic array.', 'start': 2861.41, 'duration': 1.301}, {'end': 2870.959, 'text': "I'll call this dynamic array with a lowercase d equals new dynamic array.", 'start': 2862.991, 'duration': 7.968}, {'end': 2875.003, 'text': "So I'm not going to pass in an initial capacity.", 'start': 2871.7, 'duration': 3.303}, {'end': 2878.907, 'text': "And let's print whatever the capacity is of our dynamic array.", 'start': 2875.664, 'duration': 3.243}, {'end': 2881.75, 'text': 'Dynamic array dot capacity.', 'start': 2879.267, 'duration': 2.483}, {'end': 2883.925, 'text': 'And this should be 10.', 'start': 2882.865, 'duration': 1.06}], 'summary': 'The arraylist class has a default capacity of 10 and includes overloaded constructors. the dynamic array class is created with an initial capacity of 10.', 'duration': 150.099, 'max_score': 2733.826, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2733826.jpg'}, {'end': 4387.24, 'src': 'embed', 'start': 4359.937, 'weight': 3, 'content': [{'end': 4364.518, 'text': 'So it seems that in most situations, an array list is going to be better than a linked list.', 'start': 4359.937, 'duration': 4.581}, {'end': 4371.485, 'text': "However, if you have to do a lot of inserting or deleting, especially if it's a large data set, a linked list might be better.", 'start': 4364.918, 'duration': 6.567}, {'end': 4375.809, 'text': 'But it seems that an array list is going to be more flexible for most applications.', 'start': 4371.785, 'duration': 4.024}, {'end': 4378.852, 'text': 'So that is linked lists versus array lists.', 'start': 4376.289, 'duration': 2.563}, {'end': 4382.956, 'text': 'If you would like a copy of all this code, I will post this to the comments section down below.', 'start': 4378.992, 'duration': 3.964}, {'end': 4387.24, 'text': "And well, yeah, that's linked lists versus array lists in computer science.", 'start': 4383.256, 'duration': 3.984}], 'summary': 'In most situations, an array list is better than a linked list. linked list may be better for a lot of inserting or deleting, especially with a large data set. array list is more flexible for most applications. this is linked lists versus array lists in computer science.', 'duration': 27.303, 'max_score': 4359.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s4359937.jpg'}], 'start': 2403.6, 'title': 'Dynamic arrays and memory management', 'summary': "Delves into dynamic arrays in computer science, covering their advantages, disadvantages, java custom array creation, arraylist class comparison, memory management importance, and time complexity. it also discusses dynamic array operations, including insertion, deletion, search, growing, and shrinking, and compares linked lists and array lists in java, highlighting array lists' superior performance in most cases.", 'chapters': [{'end': 2463.412, 'start': 2403.6, 'title': 'Dynamic arrays and static arrays', 'summary': 'Discusses the difference between dynamic arrays and static arrays, highlighting the resizable capacity of dynamic arrays and their alternative names in various programming languages like java, c plus plus, javascript, and python.', 'duration': 59.812, 'highlights': ['Dynamic arrays have a resizable capacity, allowing for the addition of extra elements, unlike static arrays with a fixed capacity. Resizable capacity', 'Dynamic arrays are known as array lists in java, vectors in c plus plus, arrays in javascript, and lists in python. Alternative names in different languages', 'Static arrays have a fixed capacity determined at compile time, and the capacity cannot be changed later normally. Fixed capacity']}, {'end': 3027.392, 'start': 2463.712, 'title': 'Dynamic arrays and memory management', 'summary': 'Explains the concept of dynamic arrays in computer science, highlighting their advantages and disadvantages, the process of creating a custom dynamic array using java, and the comparison with the pre-built arraylist class, while emphasizing the importance of memory management and the time complexity involved in various operations.', 'duration': 563.68, 'highlights': ['Dynamic arrays allow for random access of elements in O(1) constant time and have good locality of reference and data cache utilization due to contiguous memory addresses. Dynamic arrays provide efficient random access and utilize memory effectively due to contiguous memory addresses.', 'The process of expanding or shrinking a dynamic array involves copying all elements to a new array with different capacity, resulting in time-consuming operations. Expanding or shrinking a dynamic array requires copying all elements, leading to time-consuming operations.', 'Creating a custom dynamic array in Java involves defining overloaded constructors, add, insert, delete, search, grow, shrink, isEmpty, and toString methods. The process of creating a custom dynamic array in Java involves defining various methods to manage its functionality.']}, {'end': 3574.456, 'start': 3027.812, 'title': 'Dynamic array implementation', 'summary': 'Explains the implementation of a dynamic array in java, covering methods such as adding elements, displaying elements, checking if the array is empty, obtaining the size and capacity, inserting values at specified indices, deleting elements, and dynamically growing and shrinking the array.', 'duration': 546.644, 'highlights': ['The dynamic array has a size of three, three elements are filled in, and a capacity of 10. The dynamic array currently contains three elements with a total capacity of 10, demonstrating a 30% fill rate.', "We can insert a value at a given index, such as inserting 'X' at index zero, resulting in the array containing 'X, A, B, C' with a size of four and a capacity of 10. Demonstrates the successful insertion of a value at a specified index, showcasing the resulting updated size and capacity of the array.", 'The method explains the process of shifting all elements to the left after a deletion, including the subsequent shrinking of the array if the size falls below a third of the capacity. Illustrates the detailed process of shifting elements and dynamically shrinking the array based on a specified condition, ensuring efficient memory usage.']}, {'end': 3850.713, 'start': 3575.376, 'title': 'Dynamic array operations', 'summary': "Discusses the implementation of dynamic array operations, including insertion, deletion, search, growing, and shrinking, and their impact on the array's size and capacity.", 'duration': 275.337, 'highlights': ["The chapter explains the process of growing the dynamic array by increasing the capacity and copying elements over, thereby demonstrating the impact on the array's size and capacity. The grow method is implemented by increasing the capacity of the array by a specified factor, instantiating a new array, copying elements over, and updating the array with the new capacity. For example, increasing the capacity from 5 to 10 when adding elements beyond the current capacity.", "The chapter details the process of shrinking the array by halving the capacity and automatically calling the shrink method when the size falls below a third of the capacity to reduce wasted memory. The shrink method is implemented by halving the capacity of the array and calling it automatically when the size falls below a third of the capacity, effectively reducing wasted memory and optimizing the array's capacity. For example, shrinking the capacity from 10 to 5 after deleting elements.", 'The chapter demonstrates the search method for finding elements in the dynamic array, showing the iterative process and returning the index of the found element or -1 if not found. The search method iterates over the elements of the array, comparing them to the specified data and returning the index if found or -1 if not found, providing a way to efficiently locate elements within the dynamic array.']}, {'end': 4378.852, 'start': 3850.933, 'title': 'Comparing linked lists and array lists in java', 'summary': 'Compares the performance of linked lists and array lists in java, finding that array lists outperform linked lists in most cases, especially in accessing elements and adding or removing elements, with array lists being more flexible for most applications.', 'duration': 527.919, 'highlights': ['The chapter compares the performance of linked lists and array lists in Java, finding that array lists outperform linked lists in most cases, especially in accessing elements and adding or removing elements, with array lists being more flexible for most applications. It also includes a comparison of the time taken to access elements at different positions in both linked lists and array lists, demonstrating the efficiency of array lists over linked lists.', 'The chapter conducts tests on both array lists and linked lists in Java, populating them with a large sample size of one million elements and comparing the time taken to access elements at different positions, with array lists consistently outperforming linked lists, particularly in accessing elements at the beginning, middle, and end of the lists.', 'The chapter highlights that array lists are more efficient than linked lists in accessing elements, with array lists demonstrating faster access times compared to linked lists, especially when accessing elements at the beginning, middle, and end of the lists, making array lists more flexible and suitable for most applications.', 'The chapter demonstrates that array lists are more efficient than linked lists in adding or removing elements, with array lists showing faster performance in such operations, particularly when adding or removing elements at different positions within the lists.', "The chapter explains the reasons behind the performance differences between linked lists and array lists, attributing the efficiency of array lists to their random access of elements, while highlighting the shift operations required by array lists when adding or removing elements, and how linked lists' structure affects their performance in different scenarios."]}], 'duration': 1975.252, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s2403600.jpg', 'highlights': ['Dynamic arrays have a resizable capacity, allowing for the addition of extra elements, unlike static arrays with a fixed capacity. Resizable capacity', 'Dynamic arrays allow for random access of elements in O(1) constant time and have good locality of reference and data cache utilization due to contiguous memory addresses. Dynamic arrays provide efficient random access and utilize memory effectively due to contiguous memory addresses.', "The chapter explains the process of growing the dynamic array by increasing the capacity and copying elements over, thereby demonstrating the impact on the array's size and capacity. The grow method is implemented by increasing the capacity of the array by a specified factor, instantiating a new array, copying elements over, and updating the array with the new capacity. For example, increasing the capacity from 5 to 10 when adding elements beyond the current capacity.", 'The chapter compares the performance of linked lists and array lists in Java, finding that array lists outperform linked lists in most cases, especially in accessing elements and adding or removing elements, with array lists being more flexible for most applications. It also includes a comparison of the time taken to access elements at different positions in both linked lists and array lists, demonstrating the efficiency of array lists over linked lists.']}, {'end': 6056.562, 'segs': [{'end': 4437.234, 'src': 'embed', 'start': 4402.778, 'weight': 2, 'content': [{'end': 4404.619, 'text': 'oh yeah, big o notation.', 'start': 4402.778, 'duration': 1.841}, {'end': 4410.642, 'text': 'so a common phrase used with big o notation is how code slows as data grows.', 'start': 4404.619, 'duration': 6.023}, {'end': 4417.846, 'text': 'it describes the performance of an algorithm as the amount of data increases, and really this notation is machine independent.', 'start': 4410.642, 'duration': 7.204}, {'end': 4422.428, 'text': "What we're really focusing on is the number of steps to complete an algorithm,", 'start': 4418.246, 'duration': 4.182}, {'end': 4425.729, 'text': 'because some machines are going to run certain algorithms faster than others.', 'start': 4422.428, 'duration': 3.301}, {'end': 4429.17, 'text': 'And three, we tend to ignore smaller operations.', 'start': 4426.369, 'duration': 2.801}, {'end': 4437.234, 'text': "If we had some task that took n plus one, we would just reduce it to just n because that plus one really isn't going to make a difference.", 'start': 4429.691, 'duration': 7.543}], 'summary': 'Big o notation describes algorithm performance as data increases, focusing on steps to complete, and ignoring smaller operations.', 'duration': 34.456, 'max_score': 4402.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s4402778.jpg'}, {'end': 4538.557, 'src': 'embed', 'start': 4511.548, 'weight': 1, 'content': [{'end': 4515.931, 'text': 'So this function is going to have a runtime complexity of O of 1.', 'start': 4511.548, 'duration': 4.383}, {'end': 4517.491, 'text': "The input size doesn't matter.", 'start': 4515.931, 'duration': 1.56}, {'end': 4519.452, 'text': "The amount of data that we have really doesn't matter.", 'start': 4517.612, 'duration': 1.84}, {'end': 4522.513, 'text': "It's going to be completed in the same amount of steps.", 'start': 4519.832, 'duration': 2.681}, {'end': 4526.094, 'text': 'So this function is way better than this previous one.', 'start': 4523.073, 'duration': 3.021}, {'end': 4528.754, 'text': 'Three steps is better than a million steps.', 'start': 4526.434, 'duration': 2.32}, {'end': 4535.456, 'text': "And the reason that this isn't O because this takes three steps, is that we really don't care about smaller operations.", 'start': 4529.195, 'duration': 6.261}, {'end': 4538.557, 'text': "In the grand scheme of things, they really won't make much of a difference.", 'start': 4535.616, 'duration': 2.941}], 'summary': 'Function has o(1) runtime complexity, completing in 3 steps, superior to previous function.', 'duration': 27.009, 'max_score': 4511.548, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s4511548.jpg'}, {'end': 4771.678, 'src': 'embed', 'start': 4747.858, 'weight': 0, 'content': [{'end': 4754.722, 'text': 'Although, some of these runtime complexities are actually faster when working with a smaller dataset, so keep that in mind.', 'start': 4747.858, 'duration': 6.864}, {'end': 4758.183, 'text': "Well, that's the very basics of Big O Notation.", 'start': 4755.002, 'duration': 3.181}, {'end': 4764.006, 'text': "It's notation used to describe the performance of an algorithm as the amount of data increases.", 'start': 4758.403, 'duration': 5.603}, {'end': 4771.678, 'text': "So if you can, give this video a thumbs up, drop a random comment down below, and well, that's big O notation in computer science.", 'start': 4764.566, 'duration': 7.112}], 'summary': 'Big o notation describes algorithm performance as data increases.', 'duration': 23.82, 'max_score': 4747.858, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s4747858.jpg'}, {'end': 4813.247, 'src': 'embed', 'start': 4786.871, 'weight': 4, 'content': [{'end': 4790.695, 'text': 'the runtime complexity of a linear search is big o event.', 'start': 4786.871, 'duration': 3.824}, {'end': 4796.42, 'text': 'the larger the data set, the number of steps to complete that search will increase proportionately.', 'start': 4790.695, 'duration': 5.725}, {'end': 4802.924, 'text': 'the disadvantages of a linear search is that they are slow for large data sets, But with the advantages,', 'start': 4796.42, 'duration': 6.504}, {'end': 4806.745, 'text': 'they are fast for searches of small to medium sized data sets.', 'start': 4802.924, 'duration': 3.821}, {'end': 4808.646, 'text': "And they don't need to be sorted.", 'start': 4807.345, 'duration': 1.301}, {'end': 4813.247, 'text': "That's a huge benefit over binary searches and interpolation searches.", 'start': 4808.786, 'duration': 4.461}], 'summary': 'Linear search has o(n) complexity, slow for large data sets but fast for small to medium ones, and does not require sorting.', 'duration': 26.376, 'max_score': 4786.871, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s4786871.jpg'}, {'end': 5148.721, 'src': 'embed', 'start': 5121.946, 'weight': 6, 'content': [{'end': 5126.128, 'text': "now, a binary search isn't too efficient when working with small data sets.", 'start': 5121.946, 'duration': 4.182}, {'end': 5133.811, 'text': "however, if you're working with a large data set like one million elements, well then a binary search is actually fantastic,", 'start': 5126.128, 'duration': 7.683}, {'end': 5138.994, 'text': "because we're eliminating half of the elements we are searching through during each phase or turn.", 'start': 5133.811, 'duration': 5.183}, {'end': 5145.999, 'text': 'So if we had a million elements after the first phase, we can already disregard like half a million elements,', 'start': 5139.515, 'duration': 6.484}, {'end': 5148.721, 'text': "and then we just repeat the process until there's only one left.", 'start': 5145.999, 'duration': 2.722}], 'summary': 'Binary search efficient for large datasets, eliminating half of elements each turn.', 'duration': 26.775, 'max_score': 5121.946, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s5121946.jpg'}, {'end': 5190.154, 'src': 'embed', 'start': 5161.531, 'weight': 7, 'content': [{'end': 5167.016, 'text': 'The runtime complexity of a binary search is O The larger the data set,', 'start': 5161.531, 'duration': 5.485}, {'end': 5171.2, 'text': 'a binary search becomes more and more efficient compared to other search algorithms.', 'start': 5167.016, 'duration': 4.184}, {'end': 5174.466, 'text': "Alright, let's perform a binary search in real life now.", 'start': 5171.784, 'duration': 2.682}, {'end': 5181.269, 'text': "We'll use the built-in binary search method of arrays to begin with, and then later on we'll build our own binary search function.", 'start': 5174.626, 'duration': 6.643}, {'end': 5183.31, 'text': "So we'll need an array to work with.", 'start': 5181.83, 'duration': 1.48}, {'end': 5185.712, 'text': "Let's say we have an array of integers named array.", 'start': 5183.731, 'duration': 1.981}, {'end': 5190.154, 'text': 'int array, and the size of this array will be 100.', 'start': 5186.072, 'duration': 4.082}], 'summary': 'Binary search has o(log n) complexity, becoming more efficient with larger data sets. will implement using arrays of 100 integers.', 'duration': 28.623, 'max_score': 5161.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s5161531.jpg'}, {'end': 5490.001, 'src': 'embed', 'start': 5462.442, 'weight': 8, 'content': [{'end': 5466.025, 'text': 'And by returning negative one, that means target not found.', 'start': 5462.442, 'duration': 3.583}, {'end': 5468.568, 'text': 'And that is our binary search function.', 'start': 5466.826, 'duration': 1.742}, {'end': 5469.349, 'text': "Let's try it.", 'start': 5468.808, 'duration': 0.541}, {'end': 5472.314, 'text': 'Okay, element found at 42.', 'start': 5470.233, 'duration': 2.081}, {'end': 5479.877, 'text': "So it took us, let's see, one, two, three, four, four steps to find the number 42 within this array of 100 elements.", 'start': 5472.314, 'duration': 7.563}, {'end': 5485.399, 'text': "Now let's increase the size because binary searches do extremely well with large data sets.", 'start': 5480.317, 'duration': 5.082}, {'end': 5490.001, 'text': "So let's say we have 1 million elements and let's change this target.", 'start': 5486.239, 'duration': 3.762}], 'summary': 'Binary search found element at 42 in 4 steps within 100 elements array.', 'duration': 27.559, 'max_score': 5462.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s5462442.jpg'}, {'end': 6056.562, 'src': 'embed', 'start': 6011.973, 'weight': 9, 'content': [{'end': 6014.015, 'text': 'After the first guess, this was not correct.', 'start': 6011.973, 'duration': 2.042}, {'end': 6017.279, 'text': 'So we narrowed down our search area, then we probed again.', 'start': 6014.395, 'duration': 2.884}, {'end': 6020.722, 'text': "This still wasn't the correct answer, so we probed again, and again.", 'start': 6017.619, 'duration': 3.103}, {'end': 6023.224, 'text': 'And again, until we got the right value.', 'start': 6021.323, 'duration': 1.901}, {'end': 6025.245, 'text': "So that's an interpolation search.", 'start': 6023.584, 'duration': 1.661}, {'end': 6030.767, 'text': "It's an improvement over a binary search that is best used for uniformly distributed data.", 'start': 6025.505, 'duration': 5.262}, {'end': 6036.089, 'text': 'It guesses where a value might be based on a calculated probe result.', 'start': 6031.207, 'duration': 4.882}, {'end': 6041.471, 'text': 'If the probe is incorrect, the search area is narrowed and a new probe is calculated.', 'start': 6036.449, 'duration': 5.022}, {'end': 6051.078, 'text': 'The interpolation search has an average runtime complexity of O and a worst-case runtime complexity of O.', 'start': 6042.031, 'duration': 9.047}, {'end': 6053.88, 'text': 'This would be if our values increase exponentially.', 'start': 6051.078, 'duration': 2.802}, {'end': 6056.562, 'text': 'So yeah, that is the interpolation search.', 'start': 6054.24, 'duration': 2.322}], 'summary': 'Interpolation search is an improvement over binary search, with average and worst-case runtime complexities of o(log log n) and o(n) respectively, best used for uniformly distributed data.', 'duration': 44.589, 'max_score': 6011.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s6011973.jpg'}], 'start': 4378.992, 'title': 'Big o notation and efficient search algorithms', 'summary': 'Introduces big o notation basics, detailing algorithms with varying runtime complexities, and delves into the efficiency of binary and interpolation search algorithms, highlighting their suitability for large datasets and accurate guesses for uniformly distributed data.', 'chapters': [{'end': 4566.429, 'start': 4378.992, 'title': 'Big o notation basics in computer science', 'summary': 'Introduces the basics of big o notation in computer science, explaining its purpose, significance, and providing examples, such as the performance of algorithms as data increases and the comparison between different runtime complexities.', 'duration': 187.437, 'highlights': ['Big O notation describes the performance of an algorithm as the amount of data increases, with examples including O linear time and O constant time. The chapter explains that Big O notation describes the performance of an algorithm as the amount of data increases and provides examples of O linear time and O constant time, showcasing the impact on the number of steps required for completion.', 'The function with a runtime complexity of O constant time is highlighted as way better than the one with a runtime complexity of O linear time, as it only takes three steps regardless of the amount of data. It is emphasized that a function with a runtime complexity of O constant time is significantly better than one with a runtime complexity of O linear time, as it only takes three steps regardless of the amount of data, showcasing the efficiency of the former over the latter.', 'The chapter explains that the notation is machine independent, focusing on the number of steps to complete an algorithm, and tends to ignore smaller operations such as reducing n plus one to just n. The chapter clarifies that Big O notation is machine independent, focusing on the number of steps to complete an algorithm and tends to ignore smaller operations, exemplifying the reduction of n plus one to just n, emphasizing its emphasis on the overall algorithmic performance.']}, {'end': 5121.946, 'start': 4566.849, 'title': 'Big o notation basics & linear search', 'summary': 'Explains the basics of big o notation, detailing algorithms with varying runtime complexities such as o(1), o(log n), o(n), o(n log n), o(n^2), and o(n!). it also delves into the linear search algorithm and its advantages and disadvantages, emphasizing its suitability for small to medium-sized datasets and the lack of sorting requirement.', 'duration': 555.097, 'highlights': ['Big O Notation Basics & Varying Runtime Complexities The chapter introduces the basics of Big O Notation, covering algorithms with different runtime complexities such as O(1), O(log n), O(n), O(n log n), O(n^2), and O(n!).', 'Advantages and Disadvantages of Linear Search Algorithm The advantages and disadvantages of the linear search algorithm are detailed, emphasizing its suitability for small to medium-sized datasets and the lack of requirement for sorting.', 'Comparison of Runtime Complexities A comparison of runtime complexities is provided, highlighting the varying efficiency and speed of algorithms based on the size of the dataset, with examples of linear time and quadratic time complexity.']}, {'end': 5460.36, 'start': 5121.946, 'title': 'Binary search efficiency', 'summary': 'Explains that binary search is efficient for large datasets like one million elements as it eliminates half of the elements during each phase, with a runtime complexity of o(log n) making it more efficient compared to other search algorithms.', 'duration': 338.414, 'highlights': ['Binary search eliminates half of the elements during each phase for large datasets, making it efficient with a runtime complexity of O(log n). If working with a large data set like one million elements, a binary search is fantastic as it eliminates half of the elements during each phase, with a runtime complexity of O(log n), making it more efficient compared to other search algorithms.', 'The built-in binary search method of arrays is used to perform a binary search in real life, and the chapter demonstrates how to create a custom binary search function. The chapter demonstrates using the built-in binary search method of arrays to perform a binary search and later shows the process of creating a custom binary search function for practice.', 'The process of binary search is explained through code demonstration with an array of integers and a target value, showcasing the step-by-step implementation and checking for the presence of the target value. The transcript provides a detailed explanation of the binary search process through code demonstration, using an array of integers and a target value, showcasing the step-by-step implementation and checking for the presence of the target value.']}, {'end': 6056.562, 'start': 5462.442, 'title': 'Binary and interpolation search', 'summary': 'Demonstrates the efficiency of binary and interpolation search algorithms, with binary search taking 20 steps to find a number within 1 million elements and interpolation search providing accurate guesses for uniformly distributed data, with an average runtime complexity of o.', 'duration': 594.12, 'highlights': ['Binary search took 20 steps to find a number within 1 million elements It took 20 steps to find the number 777,000 within a 1 million element array using binary search.', 'Interpolation search provided accurate guesses for uniformly distributed data Interpolation search accurately guessed the index for numbers in a uniformly distributed data set, such as finding the number 8 on the first iteration.', 'Interpolation search has an average runtime complexity of O The interpolation search algorithm has an average runtime complexity of O, making it efficient for searching uniformly distributed data.']}], 'duration': 1677.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s4378992.jpg', 'highlights': ['Big O notation describes algorithm performance as data increases, with examples of O linear time and O constant time.', 'Function with O constant time is significantly better than O linear time, taking only three steps regardless of data size.', 'Big O notation is machine independent, focusing on steps to complete an algorithm and tends to ignore smaller operations.', 'Introduction to Big O Notation basics, covering algorithms with different runtime complexities.', 'Advantages and disadvantages of linear search algorithm, suitable for small to medium-sized datasets.', 'Comparison of runtime complexities, highlighting varying efficiency and speed based on dataset size.', 'Binary search efficiency with O(log n) complexity for large datasets, eliminating half of elements in each phase.', 'Demonstration of using built-in and custom binary search methods with code examples.', 'Binary search took 20 steps to find a number within 1 million elements.', 'Interpolation search provided accurate guesses for uniformly distributed data.', 'Interpolation search has an average runtime complexity of O, efficient for searching uniformly distributed data.']}, {'end': 7871.255, 'segs': [{'end': 6271.491, 'src': 'embed', 'start': 6244.619, 'weight': 1, 'content': [{'end': 6251.142, 'text': "In most real world applications, you'll probably use a different sorting algorithm, but this is still a good thing to learn.", 'start': 6244.619, 'duration': 6.523}, {'end': 6256.284, 'text': 'So the bubble sort algorithm has a runtime complexity of O of N squared.', 'start': 6251.382, 'duration': 4.902}, {'end': 6257.865, 'text': 'It runs in quadratic time.', 'start': 6256.404, 'duration': 1.461}, {'end': 6263.087, 'text': 'So the larger the dataset, the more and more inefficient that this sorting algorithm is going to be.', 'start': 6258.205, 'duration': 4.882}, {'end': 6268.049, 'text': "With a small data set, it's not horrible, but there's definitely better algorithms out there.", 'start': 6263.467, 'duration': 4.582}, {'end': 6271.491, 'text': "So for practice, let's create our own bubble sort algorithm.", 'start': 6268.429, 'duration': 3.062}], 'summary': 'Bubble sort has o(n^2) complexity. inefficient for large datasets.', 'duration': 26.872, 'max_score': 6244.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s6244619.jpg'}, {'end': 7067.339, 'src': 'heatmap', 'start': 6914.784, 'weight': 1, 'content': [{'end': 6922.491, 'text': 'array at index of i equals array at index of min.', 'start': 6914.784, 'duration': 7.707}, {'end': 6932.019, 'text': "then lastly, array at index of min equals temp, and that's all there is to it.", 'start': 6922.491, 'duration': 9.528}, {'end': 6938.203, 'text': 'so after running this program, Our array is now sorted via the selection sort algorithm.', 'start': 6932.019, 'duration': 6.184}, {'end': 6944.651, 'text': "Then, of course, if you need your array or collection sorted in descending order currently it's in ascending order.", 'start': 6938.684, 'duration': 5.967}, {'end': 6953.202, 'text': 'all we do is swap this greater than sign with a less than sign, and this will now be sorted in descending order, depending on what you need.', 'start': 6944.651, 'duration': 8.551}, {'end': 6955.08, 'text': 'Well, okay then, everybody.', 'start': 6953.939, 'duration': 1.141}, {'end': 6957.781, 'text': 'That is the selection sort algorithm.', 'start': 6955.18, 'duration': 2.601}, {'end': 6963.803, 'text': 'It will search through an array and keep track of the minimum value during each iteration.', 'start': 6958.261, 'duration': 5.542}, {'end': 6968.165, 'text': "At the end of each iteration, we swap variables, and that's all there is to it.", 'start': 6964.364, 'duration': 3.801}, {'end': 6971.647, 'text': 'It runs in quadratic time, big O of n squared.', 'start': 6968.566, 'duration': 3.081}, {'end': 6977.61, 'text': "It's okay with small data sets, even more so than bubble sort, and it's pretty terrible with large data sets.", 'start': 6972.087, 'duration': 5.523}, {'end': 6982.632, 'text': 'The larger the data set, the more and more inefficient that this selection sort algorithm is going to be.', 'start': 6977.97, 'duration': 4.662}, {'end': 6985.454, 'text': 'So that is the selection sort algorithm.', 'start': 6983.112, 'duration': 2.342}, {'end': 6990.358, 'text': 'If you learned something new, give this video a big fat thumbs up, drop a random comment down below.', 'start': 6985.634, 'duration': 4.724}, {'end': 6994.461, 'text': 'and well, that is the selection sort algorithm in, I guess, computer science.', 'start': 6990.358, 'duration': 4.103}, {'end': 6996.803, 'text': "What's going on, people?", 'start': 6995.502, 'duration': 1.301}, {'end': 6997.643, 'text': "It's your bro.", 'start': 6997.083, 'duration': 0.56}, {'end': 7002.667, 'text': "hope you're doing well and in this video we're going to discuss the insertion sort algorithm in computer science.", 'start': 6997.643, 'duration': 5.024}, {'end': 7006.31, 'text': 'As always, sit back, relax, and well, enjoy the show.', 'start': 7002.987, 'duration': 3.323}, {'end': 7010.73, 'text': 'Alright everybody, insertion sort.', 'start': 7008.949, 'duration': 1.781}, {'end': 7014.312, 'text': 'Now what we do with insertion sort is that we begin at index 1.', 'start': 7010.81, 'duration': 3.502}, {'end': 7020.555, 'text': 'We take the value found within, move it to some temporary storage like a variable named temp to temporarily hold it.', 'start': 7014.312, 'duration': 6.243}, {'end': 7022.636, 'text': 'We examine elements to the left.', 'start': 7020.995, 'duration': 1.641}, {'end': 7027.358, 'text': "If any elements are larger than what's within temp, we shift those elements to the right.", 'start': 7023.076, 'duration': 4.282}, {'end': 7030.579, 'text': 'So 6 is larger than 1, we shift it to the right.', 'start': 7027.658, 'duration': 2.921}, {'end': 7035.582, 'text': "If it's less than whatever's within temp, we stop or until we run out of elements.", 'start': 7031.06, 'duration': 4.522}, {'end': 7037.163, 'text': 'So we have run out of elements.', 'start': 7036.042, 'duration': 1.121}, {'end': 7041.226, 'text': 'We take this value found within temp and place it at this opening here.', 'start': 7037.403, 'duration': 3.823}, {'end': 7042.867, 'text': 'That was the first iteration.', 'start': 7041.686, 'duration': 1.181}, {'end': 7044.548, 'text': "Let's move on to iteration two.", 'start': 7043.027, 'duration': 1.521}, {'end': 7048.19, 'text': 'Take this value, place it within temp, examine the elements to the left.', 'start': 7045.068, 'duration': 3.122}, {'end': 7052.093, 'text': 'If this element is greater than temp, then we shift it to the right.', 'start': 7048.651, 'duration': 3.442}, {'end': 7055.936, 'text': "It's not, so we stop here and place this value back where it came from.", 'start': 7052.253, 'duration': 3.683}, {'end': 7058.076, 'text': 'So that was the second iteration.', 'start': 7056.576, 'duration': 1.5}, {'end': 7059.257, 'text': 'Iteration 3.', 'start': 7058.336, 'duration': 0.921}, {'end': 7062.617, 'text': 'Take this value, place it within temp, examine the elements to the left.', 'start': 7059.257, 'duration': 3.36}, {'end': 7064.998, 'text': "If they're greater than 4, we shift them to the right.", 'start': 7062.978, 'duration': 2.02}, {'end': 7067.339, 'text': '7 is larger than 4, shift it to the right.', 'start': 7065.578, 'duration': 1.761}], 'summary': 'Selection sort algorithm runs in quadratic time, big o of n squared, and is inefficient with large data sets. insertion sort algorithm shifts elements to the right based on comparison with temp.', 'duration': 152.555, 'max_score': 6914.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s6914784.jpg'}, {'end': 6982.632, 'src': 'embed', 'start': 6955.18, 'weight': 2, 'content': [{'end': 6957.781, 'text': 'That is the selection sort algorithm.', 'start': 6955.18, 'duration': 2.601}, {'end': 6963.803, 'text': 'It will search through an array and keep track of the minimum value during each iteration.', 'start': 6958.261, 'duration': 5.542}, {'end': 6968.165, 'text': "At the end of each iteration, we swap variables, and that's all there is to it.", 'start': 6964.364, 'duration': 3.801}, {'end': 6971.647, 'text': 'It runs in quadratic time, big O of n squared.', 'start': 6968.566, 'duration': 3.081}, {'end': 6977.61, 'text': "It's okay with small data sets, even more so than bubble sort, and it's pretty terrible with large data sets.", 'start': 6972.087, 'duration': 5.523}, {'end': 6982.632, 'text': 'The larger the data set, the more and more inefficient that this selection sort algorithm is going to be.', 'start': 6977.97, 'duration': 4.662}], 'summary': 'Selection sort: searches array, swaps variables, runs in o(n^2), inefficient with large data sets.', 'duration': 27.452, 'max_score': 6955.18, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s6955180.jpg'}, {'end': 7405.322, 'src': 'embed', 'start': 7379.974, 'weight': 0, 'content': [{'end': 7385.895, 'text': 'The insertion sort algorithm has a runtime complexity of O It runs in quadratic time.', 'start': 7379.974, 'duration': 5.921}, {'end': 7389.796, 'text': "It's decent with small datasets, but bad with large datasets.", 'start': 7386.315, 'duration': 3.481}, {'end': 7395.198, 'text': 'And using insertion sort tends to be preferable to both bubble sort and selection sort.', 'start': 7390.456, 'duration': 4.742}, {'end': 7405.322, 'text': 'It uses less steps than bubble sort and, in the best case scenario, insertion sort can run in O of n linear time compared to selection sort,', 'start': 7395.758, 'duration': 9.564}], 'summary': 'Insertion sort has o(n^2) runtime complexity, better than bubble and selection sorts for small datasets.', 'duration': 25.348, 'max_score': 7379.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s7379974.jpg'}, {'end': 7502.754, 'src': 'embed', 'start': 7474.82, 'weight': 3, 'content': [{'end': 7477.362, 'text': "let's create a method to simulate walking.", 'start': 7474.82, 'duration': 2.542}, {'end': 7480.103, 'text': "we'll do this both iteratively and recursively.", 'start': 7477.362, 'duration': 2.741}, {'end': 7482.504, 'text': 'then take a look at the differences between the two.', 'start': 7480.103, 'duration': 2.401}, {'end': 7485.466, 'text': "so let's write an iterative walk method.", 'start': 7482.504, 'duration': 2.962}, {'end': 7491.869, 'text': 'so we will need to invoke this method and then we will pass in the amount of steps that we would like to walk, like five steps,', 'start': 7485.466, 'duration': 6.403}, {'end': 7494.19, 'text': "And then we'll need to declare this method.", 'start': 7492.389, 'duration': 1.801}, {'end': 7496.011, 'text': 'Private static void walk.', 'start': 7494.57, 'duration': 1.441}, {'end': 7498.472, 'text': "And I'll change i to maybe steps.", 'start': 7496.191, 'duration': 2.281}, {'end': 7500.093, 'text': "Just so that it's more descriptive.", 'start': 7498.872, 'duration': 1.221}, {'end': 7502.754, 'text': "Now let's use an iterative approach.", 'start': 7500.793, 'duration': 1.961}], 'summary': 'Create iterative walk method with 5 steps.', 'duration': 27.934, 'max_score': 7474.82, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s7474820.jpg'}], 'start': 6056.782, 'title': 'Sorting algorithms and iterative vs recursive methods', 'summary': 'Discusses interpolation search, bubble sort, selection sort, and insertion sort algorithms, emphasizing their processes, runtime complexities, and efficiency for different dataset sizes, along with a comparison of iterative and recursive methods in terms of approach, efficiency, and memory usage.', 'chapters': [{'end': 6165.818, 'start': 6056.782, 'title': 'Interpolation search and bubble sort', 'summary': 'Discusses the interpolation search and bubble sort algorithms, explaining their processes with an example and emphasizing the manual sorting of nine elements using the bubble sort algorithm.', 'duration': 109.036, 'highlights': ['The chapter discusses the interpolation search and bubble sort algorithms It provides an explanation of the interpolation search and bubble sort algorithms, demonstrating their processes and comparing adjacent elements for sorting.', 'Explaining the manual sorting of nine elements using the bubble sort algorithm The transcript emphasizes the manual sorting of nine unordered elements using the bubble sort algorithm, detailing the comparison of adjacent elements and the process of swapping elements to achieve ascending numeric order.', 'Demonstrating the process of swapping variables in the bubble sort algorithm The transcript explains the process of swapping variables in the bubble sort algorithm, emphasizing the use of a temporary variable and referring to a related video in the Java playlist.']}, {'end': 6518.706, 'start': 6166.399, 'title': 'Bubble sort algorithm', 'summary': "Introduces the bubble sort algorithm with a runtime complexity of o of n squared, highlighting its inefficiency for larger datasets and recommending it for small datasets, along with a demonstration of the algorithm's implementation in code.", 'duration': 352.307, 'highlights': ['The bubble sort algorithm has a runtime complexity of O of N squared, making it inefficient for larger datasets. The bubble sort algorithm has a runtime complexity of O of N squared, leading to inefficiency for larger datasets.', 'The chapter demonstrates the implementation of the bubble sort algorithm in code using nested for loops and temporary variable for swapping elements. The chapter demonstrates the implementation of the bubble sort algorithm in code using nested for loops and temporary variable for swapping elements.', 'The bubble sort algorithm is recommended for small datasets due to its simplicity in implementation despite its inefficiency. The bubble sort algorithm is recommended for small datasets due to its simplicity in implementation despite its inefficiency.']}, {'end': 6806.795, 'start': 6518.706, 'title': 'Understanding selection sort algorithm', 'summary': 'Discusses the selection sort algorithm, which involves iteratively finding the minimum value in an unsorted array and swapping it with the first unsorted element. it also demonstrates the process of implementing a selection sort algorithm and highlights its runtime complexity of o(n).', 'duration': 288.089, 'highlights': ['The selection sort algorithm involves iteratively finding the minimum value in an unsorted array and swapping it with the first unsorted element. The process of identifying the minimum value and swapping it with the first unsorted element is explained through the analogy of searching through closed boxes in an attic.', "The runtime complexity of the selection sort algorithm is O(n), making it inefficient for larger data sets. The algorithm's runtime complexity is highlighted as O(n) and its inefficiency for larger data sets is mentioned.", 'The process of implementing a selection sort algorithm is demonstrated using an array of integers and a forEach loop to iterate over the elements. The steps for implementing a selection sort algorithm using an array of integers and a forEach loop for iteration are explained.']}, {'end': 7020.555, 'start': 6806.796, 'title': 'Selection sort algorithm', 'summary': 'Explains the selection sort algorithm, which iterates through an array, keeping track of the minimum value, and runs in quadratic time with a big o of n squared, being inefficient with large data sets.', 'duration': 213.759, 'highlights': ['The selection sort algorithm runs in quadratic time with a big O of n squared, making it inefficient with large data sets. The selection sort algorithm runs in quadratic time with a big O of n squared, being inefficient with large data sets.', 'The selection sort algorithm iterates through an array, keeping track of the minimum value during each iteration. The selection sort algorithm iterates through an array, keeping track of the minimum value during each iteration.', 'The chapter explains the selection sort algorithm, which sorts the array in ascending order and can be adjusted for descending order by swapping comparison signs. The selection sort algorithm sorts the array in ascending order and can be adjusted for descending order by swapping comparison signs.']}, {'end': 7474.82, 'start': 7020.995, 'title': 'Insertion sort algorithm', 'summary': 'Explains the insertion sort algorithm, which compares elements to the left and shifts elements to the right to make room to insert a value. the algorithm has a runtime complexity of o(n^2), is decent with small datasets but bad with large datasets, and is preferable to both bubble sort and selection sort due to its efficiency.', 'duration': 453.825, 'highlights': ['The insertion sort algorithm compares elements to the left and it will shift elements to the right to make room to insert a value. The algorithm compares elements to the left and shifts elements to the right to make room for inserting a value.', 'The insertion sort algorithm has a runtime complexity of O(n^2). The algorithm has a runtime complexity of O(n^2), making it efficient with small datasets but inefficient with large datasets.', 'Using insertion sort tends to be preferable to both bubble sort and selection sort. Insertion sort is preferred over bubble sort and selection sort due to its efficiency, using fewer steps than bubble sort and having a better best-case scenario runtime complexity than selection sort.', 'Recursion is when a thing is defined in terms of itself. A recursive method is one that calls itself. Recursion is defined as a method calling itself, and it can be used as a substitute for iteration.', 'With recursion, we divide a problem into subproblems of the same type as the original, and it tends to be used within advanced sorting algorithms and in navigating trees. Recursion involves dividing a problem into subproblems of the same type as the original, and it is often used in advanced sorting algorithms and navigating trees.']}, {'end': 7871.255, 'start': 7474.82, 'title': 'Recursive vs iterative methods', 'summary': 'Compares iterative and recursive methods for simulating walking and finding factorial, emphasizing the differences in approach, efficiency, and memory usage, and also demonstrates a recursive method for finding a base raised to a given power.', 'duration': 396.435, 'highlights': ["The chapter compares iterative and recursive methods for simulating walking and finding factorial, emphasizing the differences in approach, efficiency, and memory usage. The chapter demonstrates the iterative and recursive methods for simulating walking, highlighting the differences in approach, efficiency, and memory usage. It also explores the iterative approach's use of a for loop, while the recursive approach faces potential stack overflow issues.", 'The chapter demonstrates a recursive method for finding a base raised to a given power. The chapter demonstrates a recursive method for finding a base raised to a given power, illustrating the use of a base case and a recursive case to calculate the result, with a specific example of 2 to the power of 8 equalling 256.', 'The chapter explains the impact of recursion on memory usage and performance, using a practical example of finding the factorial of 7 with a recursive approach. The chapter explains the impact of recursion on memory usage and performance, using a practical example of finding the factorial of 7 with a recursive approach, highlighting the ease of writing such code while also noting the potential for slower execution and higher memory usage.']}], 'duration': 1814.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s6056782.jpg', 'highlights': ['The insertion sort algorithm has a runtime complexity of O(n^2).', 'The bubble sort algorithm has a runtime complexity of O of N squared, making it inefficient for larger datasets.', 'The selection sort algorithm involves iteratively finding the minimum value in an unsorted array and swapping it with the first unsorted element.', 'The chapter compares iterative and recursive methods for simulating walking and finding factorial, emphasizing the differences in approach, efficiency, and memory usage.']}, {'end': 9527.89, 'segs': [{'end': 7919.948, 'src': 'embed', 'start': 7894.069, 'weight': 3, 'content': [{'end': 7899.256, 'text': 'and recursion is commonly used within advanced sorting algorithms and navigating trees.', 'start': 7894.069, 'duration': 5.187}, {'end': 7905.282, 'text': 'Some of the advantages is that recursive code is easier to read and write, and easier to debug.', 'start': 7899.737, 'duration': 5.545}, {'end': 7908.544, 'text': "However, it's sometimes slower and uses more memory.", 'start': 7905.723, 'duration': 2.821}, {'end': 7910.444, 'text': 'So yeah, that is recursion.', 'start': 7909.044, 'duration': 1.4}, {'end': 7913.425, 'text': 'If you learned something new, be sure to smash that like button.', 'start': 7910.644, 'duration': 2.781}, {'end': 7915.066, 'text': 'leave a random comment down below.', 'start': 7913.425, 'duration': 1.641}, {'end': 7917.867, 'text': "and well, yeah, that's recursion in computer science.", 'start': 7915.066, 'duration': 2.801}, {'end': 7919.948, 'text': "Hey, what's going on everybody?", 'start': 7918.627, 'duration': 1.321}], 'summary': 'Recursion is used in advanced sorting, navigation; easier to read/write, but slower with more memory.', 'duration': 25.879, 'max_score': 7894.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s7894069.jpg'}, {'end': 8209.653, 'src': 'embed', 'start': 8179.118, 'weight': 1, 'content': [{'end': 8184.58, 'text': 'It runs in quasi-linear time, along with quick sort and heap sort, which we still need to talk about.', 'start': 8179.118, 'duration': 5.462}, {'end': 8191.403, 'text': 'So when working with large data sets, merge sort is faster than insertion sort, selection sort, and bubble sort.', 'start': 8184.8, 'duration': 6.603}, {'end': 8198.687, 'text': 'But on the other hand, the merge sort algorithm uses more space than bubble sort, selection sort and insertion sort,', 'start': 8191.963, 'duration': 6.724}, {'end': 8201.829, 'text': 'because we need to create new subarrays to store elements.', 'start': 8198.687, 'duration': 3.142}, {'end': 8209.653, 'text': 'Whereas bubble sort, selection sort and insertion sort can sort in place, so they use a constant amount of space to do their sorting,', 'start': 8202.308, 'duration': 7.345}], 'summary': 'Merge sort is faster for large data sets, but uses more space than other sorts.', 'duration': 30.535, 'max_score': 8179.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8179118.jpg'}, {'end': 8255.423, 'src': 'embed', 'start': 8233.94, 'weight': 5, 'content': [{'end': 8243.044, 'text': 'this is going to be a recursive method and we will pass in an array and each time that we invoke this method, we will split our array in half,', 'start': 8233.94, 'duration': 9.104}, {'end': 8246.385, 'text': 'create two sub arrays and then copy the elements over.', 'start': 8243.044, 'duration': 3.341}, {'end': 8251.441, 'text': "so let's create and declare this method Private static void merge sort.", 'start': 8246.385, 'duration': 5.056}, {'end': 8253.702, 'text': "And we'll need a helper method too.", 'start': 8251.922, 'duration': 1.78}, {'end': 8255.423, 'text': "And we'll name this merge.", 'start': 8254.423, 'duration': 1}], 'summary': 'Recursive merge sort method splits array in half and creates sub arrays.', 'duration': 21.483, 'max_score': 8233.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8233940.jpg'}, {'end': 8318.168, 'src': 'embed', 'start': 8288.141, 'weight': 6, 'content': [{'end': 8291.282, 'text': "So let's cache that within a local variable named length.", 'start': 8288.141, 'duration': 3.141}, {'end': 8296.165, 'text': 'Int length equals array dot length.', 'start': 8292.624, 'duration': 3.541}, {'end': 8299.107, 'text': "And we'll need a base case too.", 'start': 8297.867, 'duration': 1.24}, {'end': 8306.941, 'text': 'When do we stop recursion? If Length is less than or equal to 1.', 'start': 8299.207, 'duration': 7.734}, {'end': 8308.022, 'text': 'Then we shall return.', 'start': 8306.941, 'duration': 1.081}, {'end': 8309.963, 'text': 'And this is our base case.', 'start': 8308.742, 'duration': 1.221}, {'end': 8314.366, 'text': "Basically, with this merge sort method, we're dividing our array in two each time.", 'start': 8310.282, 'duration': 4.084}, {'end': 8318.168, 'text': "If the length is 1, there's no longer a need to divide our array further.", 'start': 8314.706, 'duration': 3.462}], 'summary': 'Implementing merge sort method to divide array into two each time, stopping recursion when length is less than or equal to 1.', 'duration': 30.027, 'max_score': 8288.141, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8288141.jpg'}, {'end': 8508.194, 'src': 'embed', 'start': 8475.185, 'weight': 7, 'content': [{'end': 8477.506, 'text': 'So that is it for the merge sort function.', 'start': 8475.185, 'duration': 2.321}, {'end': 8478.946, 'text': "Let's work on merge next.", 'start': 8477.566, 'duration': 1.38}, {'end': 8485.95, 'text': "First thing that we're going to do within the merge method is cache the size of our left array and right array within some local variables.", 'start': 8479.407, 'duration': 6.543}, {'end': 8492.613, 'text': 'Int left size equals array dot length divided by two.', 'start': 8486.57, 'duration': 6.043}, {'end': 8502.591, 'text': 'Int right size equals equals array dot length minus left size.', 'start': 8493.753, 'duration': 8.838}, {'end': 8505.572, 'text': "and then we'll need three indices.", 'start': 8502.591, 'duration': 2.981}, {'end': 8508.194, 'text': 'int i equals zero.', 'start': 8505.572, 'duration': 2.622}], 'summary': 'Working on the merge method, caching sizes of left and right arrays and initializing indices.', 'duration': 33.009, 'max_score': 8475.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8475185.jpg'}, {'end': 8939.366, 'src': 'heatmap', 'start': 8650.356, 'weight': 0.776, 'content': [{'end': 8651.776, 'text': "Then we'll need another while loop.", 'start': 8650.356, 'duration': 1.42}, {'end': 8660.238, 'text': 'If r is less than right size, we will copy the last right element over.', 'start': 8652.676, 'duration': 7.562}, {'end': 8669.08, 'text': 'Array at index of i equals right array at index of r.', 'start': 8661.158, 'duration': 7.922}, {'end': 8672.52, 'text': 'Increment i, increment r.', 'start': 8669.08, 'duration': 3.44}, {'end': 8673.341, 'text': 'And that should be it.', 'start': 8672.52, 'duration': 0.821}, {'end': 8675.061, 'text': "Let's run this.", 'start': 8674.421, 'duration': 0.64}, {'end': 8679.1, 'text': 'And our array is now sorted.', 'start': 8677.498, 'duration': 1.602}, {'end': 8687.468, 'text': 'In conclusion, everybody, the merge sort algorithm recursively divides an array in two, sorts them, and then recombines them.', 'start': 8679.78, 'duration': 7.688}, {'end': 8698.977, 'text': 'The merge sort algorithm has a runtime complexity of O log n and a space complexity of O So that is the merge sort algorithm.', 'start': 8687.928, 'duration': 11.049}, {'end': 8703.02, 'text': 'If you would like a copy of this code, I will post this to the comment section down below.', 'start': 8699.118, 'duration': 3.902}, {'end': 8707.623, 'text': 'And well, yeah, that is the merge sort algorithm in computer science.', 'start': 8703.42, 'duration': 4.203}, {'end': 8709.584, 'text': "Hey, it's your bro.", 'start': 8708.403, 'duration': 1.181}, {'end': 8710.524, 'text': "Hope you're doing well.", 'start': 8709.684, 'duration': 0.84}, {'end': 8714.707, 'text': "And in this video, I'm going to explain the quick sort algorithm in computer science.", 'start': 8710.684, 'duration': 4.023}, {'end': 8718.269, 'text': 'Yep So sit back, relax, and enjoy the show.', 'start': 8714.967, 'duration': 3.302}, {'end': 8722.301, 'text': 'All right, quicksort.', 'start': 8720.86, 'duration': 1.441}, {'end': 8724.221, 'text': "Here's how quicksort is going to work.", 'start': 8722.381, 'duration': 1.84}, {'end': 8727.122, 'text': 'We need an array or some other type of collection.', 'start': 8724.661, 'duration': 2.461}, {'end': 8729.343, 'text': 'I have a simple unordered array.', 'start': 8727.382, 'duration': 1.961}, {'end': 8734.765, 'text': "What we do is that we'll pass our array as an argument into a quicksort function.", 'start': 8729.563, 'duration': 5.202}, {'end': 8740.947, 'text': 'After passing our array as an argument to the quicksort function, we need to pick a pivot.', 'start': 8735.305, 'duration': 5.642}, {'end': 8743.268, 'text': "There's different variations of quicksort.", 'start': 8741.227, 'duration': 2.041}, {'end': 8747.209, 'text': 'We can either pick a pivot at the beginning, the middle, or at the end.', 'start': 8743.328, 'duration': 3.881}, {'end': 8753.496, 'text': 'But with most standard quicksort algorithms, we will set the pivot to be at the end of our array.', 'start': 8747.349, 'duration': 6.147}, {'end': 8759.343, 'text': "What we're trying to accomplish is that we need to find the final resting place of our pivot.", 'start': 8753.677, 'duration': 5.666}, {'end': 8761.226, 'text': 'Where is this value going to be?', 'start': 8759.544, 'duration': 1.682}, {'end': 8767.109, 'text': "And taking a look at this, that would be right about here, but we don't know that yet.", 'start': 8761.526, 'duration': 5.583}, {'end': 8772.351, 'text': "so to find the final resting place of this value, our pivot, here's what we can do.", 'start': 8767.109, 'duration': 5.242}, {'end': 8776.493, 'text': 'we will declare and use two indices j and i.', 'start': 8772.351, 'duration': 4.142}, {'end': 8779.234, 'text': 'j will begin at the start of our array.', 'start': 8776.493, 'duration': 2.741}, {'end': 8786.456, 'text': "i will be one less than the beginning of our array, and that's going to be important later, and we'll need the help of a temporary variable,", 'start': 8779.234, 'duration': 7.222}, {'end': 8787.857, 'text': 'so we can swap some values.', 'start': 8786.456, 'duration': 1.401}, {'end': 8793.08, 'text': "All we're doing is checking to see if the value at j is less than our pivot.", 'start': 8788.237, 'duration': 4.843}, {'end': 8796.822, 'text': "If it's greater than our pivot, or equal to our pivot, we ignore it.", 'start': 8793.38, 'duration': 3.442}, {'end': 8803.945, 'text': '8 is greater than 5, we ignore this value, and then increment j by 1.', 'start': 8797.322, 'duration': 6.623}, {'end': 8807.967, 'text': 'So during that last iteration, i did not come into play yet, but it will this round.', 'start': 8803.945, 'duration': 4.022}, {'end': 8812.47, 'text': 'Again, we check to see if this value is less than our pivot, which it is.', 'start': 8808.388, 'duration': 4.082}, {'end': 8814.611, 'text': 'What we do now is increment i.', 'start': 8812.71, 'duration': 1.901}, {'end': 8823.042, 'text': "Then what comes next is that we swap these two values, i and j, and we'll need the help of a temporary variable.", 'start': 8816.078, 'duration': 6.964}, {'end': 8826.544, 'text': 'So take the value at i, assign it to temp.', 'start': 8823.442, 'duration': 3.102}, {'end': 8833.008, 'text': 'Take the value at j, assign it to i.', 'start': 8827.865, 'duration': 5.143}, {'end': 8837.05, 'text': 'Take the value within temp, assign it to j.', 'start': 8833.008, 'duration': 4.042}, {'end': 8838.991, 'text': 'Then we can move on to the next iteration.', 'start': 8837.05, 'duration': 1.941}, {'end': 8839.952, 'text': 'Increment j.', 'start': 8839.451, 'duration': 0.501}, {'end': 8846.212, 'text': 'We check to see if the value at j is less than our pivot, which it is.', 'start': 8842.128, 'duration': 4.084}, {'end': 8851.899, 'text': 'If that is the case, we increment i, swap these two values.', 'start': 8846.733, 'duration': 5.166}, {'end': 8861.95, 'text': "We'll repeat this process until j reaches our pivot, then we can move on to the next step.", 'start': 8856.644, 'duration': 5.306}, {'end': 8897.902, 'text': "Here's the next step after our index J reaches our pivot.", 'start': 8893.84, 'duration': 4.062}, {'end': 8902.145, 'text': 'We now know where the final resting place of our pivot is gonna be.', 'start': 8898.363, 'duration': 3.782}, {'end': 8904.346, 'text': "It's I incremented by one.", 'start': 8902.465, 'duration': 1.881}, {'end': 8911.811, 'text': 'So we will increment I, then swap the value at index of I with the value at our pivot.', 'start': 8904.526, 'duration': 7.285}, {'end': 8918.65, 'text': 'Our pivot is now within the correct place.', 'start': 8916.568, 'duration': 2.082}, {'end': 8928.198, 'text': "An easy way to tell is that all elements to the left of our pivot should be less than our pivot, but they're not necessarily going to be in order,", 'start': 8919.25, 'duration': 8.948}, {'end': 8928.839, 'text': "and that's fine.", 'start': 8928.198, 'duration': 0.641}, {'end': 8930.12, 'text': "They'll be organized later.", 'start': 8928.959, 'duration': 1.161}, {'end': 8935.965, 'text': 'All elements to the right of our pivot should be greater than or equal to our pivot.', 'start': 8930.82, 'duration': 5.145}, {'end': 8939.366, 'text': 'And like I said before, they probably will not be in order.', 'start': 8936.485, 'duration': 2.881}], 'summary': 'The merge sort algorithm recursively divides an array in two, sorts them, and then recombines them with a runtime complexity of o log n and a space complexity of o. the quick sort algorithm selects a pivot, finds its final resting place, and organizes elements around it.', 'duration': 289.01, 'max_score': 8650.356, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8650356.jpg'}, {'end': 8703.02, 'src': 'embed', 'start': 8679.78, 'weight': 0, 'content': [{'end': 8687.468, 'text': 'In conclusion, everybody, the merge sort algorithm recursively divides an array in two, sorts them, and then recombines them.', 'start': 8679.78, 'duration': 7.688}, {'end': 8698.977, 'text': 'The merge sort algorithm has a runtime complexity of O log n and a space complexity of O So that is the merge sort algorithm.', 'start': 8687.928, 'duration': 11.049}, {'end': 8703.02, 'text': 'If you would like a copy of this code, I will post this to the comment section down below.', 'start': 8699.118, 'duration': 3.902}], 'summary': 'Merge sort algorithm has o(log n) runtime complexity and o space complexity.', 'duration': 23.24, 'max_score': 8679.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8679780.jpg'}, {'end': 8983.883, 'src': 'embed', 'start': 8949.75, 'weight': 10, 'content': [{'end': 8953.112, 'text': "We're going to create two sections, two partitions.", 'start': 8949.75, 'duration': 3.362}, {'end': 8961.234, 'text': 'The first partition will be all the elements from the beginning of our array up until our pivot, but not including the pivot.', 'start': 8953.692, 'duration': 7.542}, {'end': 8967.316, 'text': 'And our second partition will be all the elements after our pivot until the end of our array.', 'start': 8961.674, 'duration': 5.642}, {'end': 8970.056, 'text': 'Quicksort is a recursive algorithm.', 'start': 8967.696, 'duration': 2.36}, {'end': 8975.878, 'text': 'We need to pass these partitions as arguments into the quicksort function.', 'start': 8970.436, 'duration': 5.442}, {'end': 8983.883, 'text': 'remember that the quicksort algorithm is a recursive divide and conquer algorithm, but, unlike with merge sort,', 'start': 8976.238, 'duration': 7.645}], 'summary': 'Creating two partitions for quicksort algorithm.', 'duration': 34.133, 'max_score': 8949.75, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8949750.jpg'}, {'end': 9170.395, 'src': 'embed', 'start': 8996.391, 'weight': 8, 'content': [{'end': 9003.88, 'text': "And then it's just a matter of repeating the same steps over again, but we're going to instead use these partitions sections of our array.", 'start': 8996.391, 'duration': 7.489}, {'end': 9008.466, 'text': "I'll give you a quick demonstration of what the quicksort algorithm is going to look like to completion.", 'start': 9004.02, 'duration': 4.446}, {'end': 9092.952, 'text': 'so so Thank you.', 'start': 9058.991, 'duration': 33.961}, {'end': 9170.395, 'text': 'And that, ladies and gentlemen, was your visual representation of the quicksort algorithm.', 'start': 9165.53, 'duration': 4.865}], 'summary': 'Demo of quicksort algorithm using partitioned array sections.', 'duration': 174.004, 'max_score': 8996.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s8996391.jpg'}, {'end': 9527.89, 'src': 'heatmap', 'start': 9368.041, 'weight': 11, 'content': [{'end': 9379.888, 'text': 'int j equals start, and we will continue this for loop as long as j is less than or equal to the end of our array minus 1, then increment j by 1.', 'start': 9368.041, 'duration': 11.847}, {'end': 9387.633, 'text': "What we're going to check is if array at index of j is less than our pivot.", 'start': 9379.888, 'duration': 7.745}, {'end': 9393.213, 'text': 'If one of these elements is less than our pivot, we want it on the left hand side of our pivot.', 'start': 9388.709, 'duration': 4.504}, {'end': 9397.017, 'text': 'Any numbers larger than our pivot should be on the right hand side.', 'start': 9393.994, 'duration': 3.023}, {'end': 9401.602, 'text': 'So we will increment i by 1 and do a basic variable swap.', 'start': 9397.718, 'duration': 3.884}, {'end': 9407.607, 'text': "And we'll need the help of a temporary variable int temp equals array at index of i.", 'start': 9401.862, 'duration': 5.745}, {'end': 9415.459, 'text': 'array at index of i equals array at index of j.', 'start': 9408.795, 'duration': 6.664}, {'end': 9419.562, 'text': 'lastly, array at index of j equals temp.', 'start': 9415.459, 'duration': 4.103}, {'end': 9422.263, 'text': 'this is just a basic variable swap.', 'start': 9419.562, 'duration': 2.701}, {'end': 9430.208, 'text': 'once all elements that are less than our pivot are on the left hand side and all elements that are larger than our pivot are on the right hand side,', 'start': 9422.263, 'duration': 7.945}, {'end': 9439.618, 'text': 'what we will do now is increment i by one and then insert our pivot into its final resting place with another basic variable swap.', 'start': 9430.208, 'duration': 9.41}, {'end': 9444.023, 'text': "So let's copy this code, then paste it, and make a few changes.", 'start': 9440.118, 'duration': 3.905}, {'end': 9447.727, 'text': "int temp equals array at index of i, that's the same.", 'start': 9444.584, 'duration': 3.143}, {'end': 9453.008, 'text': 'Array at index of i equals array at index of end.', 'start': 9448.843, 'duration': 4.165}, {'end': 9456.633, 'text': 'Array at index of end equals temp.', 'start': 9453.689, 'duration': 2.944}, {'end': 9457.674, 'text': "And that's it.", 'start': 9457.093, 'duration': 0.581}, {'end': 9459.997, 'text': 'And then make sure you return i at the end.', 'start': 9458.174, 'duration': 1.823}, {'end': 9462.199, 'text': 'That is the location of our pivot.', 'start': 9460.157, 'duration': 2.042}, {'end': 9468.607, 'text': 'Then after running this, Our array is now sorted via the quicksort algorithm.', 'start': 9462.86, 'duration': 5.747}, {'end': 9474.33, 'text': 'In conclusion, the quicksort algorithm moves smaller elements to the left of a pivot.', 'start': 9469.108, 'duration': 5.222}, {'end': 9484.233, 'text': 'We recursively divide our array into two partitions and pass those partitions as arguments recursively into the quicksort function.', 'start': 9474.53, 'duration': 9.703}, {'end': 9488.115, 'text': 'The runtime complexity of the quicksort algorithm actually varies.', 'start': 9484.433, 'duration': 3.682}, {'end': 9491.876, 'text': 'In its best and average cases, it runs in O log n.', 'start': 9488.475, 'duration': 3.401}, {'end': 9502.489, 'text': 'However, in its worst case, it can run in O This is rare and it occurs if the array is already sorted or close to being sorted.', 'start': 9493.757, 'duration': 8.732}, {'end': 9512.402, 'text': 'But most of the time it will run in O And the space complexity of the quicksort algorithm is O This is due to recursion.', 'start': 9502.669, 'duration': 9.733}, {'end': 9518.965, 'text': 'It uses more space than bubble sort, selection sort, and insertion sort, even though it sorts in place.', 'start': 9512.862, 'duration': 6.103}, {'end': 9522.307, 'text': "That's because the quicksort algorithm uses recursion.", 'start': 9519.266, 'duration': 3.041}, {'end': 9525.349, 'text': "We're adding frames to the call stack, which takes memory.", 'start': 9522.668, 'duration': 2.681}, {'end': 9527.89, 'text': 'So yeah, that is the quicksort algorithm.', 'start': 9525.689, 'duration': 2.201}], 'summary': 'Quicksort algorithm sorts in o(log n) time, with o(n) space complexity due to recursion.', 'duration': 53.36, 'max_score': 9368.041, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s9368041.jpg'}], 'start': 7871.255, 'title': 'Sorting algorithms in java', 'summary': 'Covers merge sort and quicksort algorithms, their implementations in java, emphasizing key concepts, complexities, and comparisons, aiding understanding of sorting algorithms in computer science.', 'chapters': [{'end': 8220.327, 'start': 7871.255, 'title': 'Merge sort algorithm', 'summary': 'Discusses the concept of recursion in computer science, emphasizing its application in advanced sorting algorithms and navigating trees. it then delves into the merge sort algorithm, explaining its divide and conquer approach, recursive nature, and runtime complexity of o log n, along with comparisons to other sorting algorithms in terms of speed and space usage.', 'duration': 349.072, 'highlights': ['The merge sort algorithm has a runtime complexity of O log n. Merge sort has a runtime complexity of O log n, making it efficient for large data sets.', 'Merge sort is faster than insertion sort, selection sort, and bubble sort when working with large data sets. When dealing with large data sets, merge sort outperforms insertion sort, selection sort, and bubble sort in terms of speed.', 'Merge sort uses more space than bubble sort, selection sort, and insertion sort due to the creation of new subarrays. Unlike bubble sort, selection sort, and insertion sort, merge sort requires more space as it creates new subarrays to store elements for sorting.', 'Recursion is commonly used within advanced sorting algorithms and navigating trees. Recursion finds common use in advanced sorting algorithms and navigating trees, allowing for the division of problems into subproblems of the same type as the original.', 'Recursive code is easier to read and write, and easier to debug. Recursive code offers advantages in terms of readability, writability, and debuggability.']}, {'end': 8679.1, 'start': 8220.327, 'title': 'Merge sort algorithm in java', 'summary': 'Discusses the implementation of a merge sort algorithm in java, explaining the recursive method to split, copy elements, and merge subarrays, with a focus on dividing the array and merging the elements back into order.', 'duration': 458.773, 'highlights': ['The chapter discusses the implementation of a merge sort algorithm in Java, explaining the recursive method to split, copy elements, and merge subarrays. Explanation of the recursive method, splitting the array, creating subarrays, and merging elements.', 'The method involves dividing the array in two each time, with a base case of stopping recursion when the length is less than or equal to 1. Explanation of the base case for stopping recursion and the process of dividing the array.', 'Detailed explanation of the merge method, including caching the size of left and right arrays, indices tracking, and conditions for merging elements. Detailed explanation of the merge method, including the process of merging elements from left and right arrays.']}, {'end': 9246.561, 'start': 8679.78, 'title': 'Understanding quicksort algorithm', 'summary': 'Explains the quicksort algorithm in computer science, detailing its process, including picking a pivot, finding its final resting place, creating partitions, and demonstrating a visual representation, all to solidify understanding of the topic.', 'duration': 566.781, 'highlights': ['The quicksort algorithm divides an array by picking a pivot, finding its final resting place using indices, and creating partitions, thus demonstrating a visual representation to solidify understanding of the topic.', 'The quicksort algorithm involves picking a pivot, finding its final resting place using indices, and creating partitions, all illustrated through a visual representation to solidify understanding of the topic.', 'The merge sort algorithm recursively divides an array in two, sorts them, and recombines them with a runtime complexity of O log n and a space complexity of O.', 'The merge sort algorithm has a runtime complexity of O log n and a space complexity of O, making it an important algorithm in computer science.', 'The merge sort algorithm has a runtime complexity of O log n and a space complexity of O, making it a critical concept to understand in computer science.']}, {'end': 9527.89, 'start': 9247.162, 'title': 'Quicksort algorithm', 'summary': 'Explains the implementation of the quicksort algorithm, which recursively divides the array into two partitions, and sorts in o(log n) in best and average cases but can run in o(n^2) in the worst case. additionally, the space complexity is o(n) due to recursion.', 'duration': 280.728, 'highlights': ['The quicksort algorithm moves smaller elements to the left of a pivot and recursively divides the array into two partitions, passing them as arguments into the quicksort function.', 'The runtime complexity of the quicksort algorithm is O(log n) in its best and average cases, but can run in O(n^2) in its worst case, which occurs if the array is already sorted or close to being sorted.', 'The space complexity of the quicksort algorithm is O(n) due to recursion, as it uses more space than other sorting algorithms like bubble sort, selection sort, and insertion sort.']}], 'duration': 1656.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s7871255.jpg', 'highlights': ['Merge sort has a runtime complexity of O log n, making it efficient for large data sets.', 'Merge sort outperforms insertion sort, selection sort, and bubble sort in terms of speed for large data sets.', 'Merge sort requires more space as it creates new subarrays to store elements for sorting.', 'Recursion finds common use in advanced sorting algorithms and navigating trees.', 'Recursive code offers advantages in terms of readability, writability, and debuggability.', 'The chapter discusses the implementation of a merge sort algorithm in Java, explaining the recursive method to split, copy elements, and merge subarrays.', 'The method involves dividing the array in two each time, with a base case of stopping recursion when the length is less than or equal to 1.', 'Detailed explanation of the merge method, including caching the size of left and right arrays, indices tracking, and conditions for merging elements.', 'The quicksort algorithm divides an array by picking a pivot, finding its final resting place using indices, and creating partitions, thus demonstrating a visual representation to solidify understanding of the topic.', 'The merge sort algorithm has a runtime complexity of O log n and a space complexity of O, making it an important algorithm in computer science.', 'The quicksort algorithm moves smaller elements to the left of a pivot and recursively divides the array into two partitions, passing them as arguments into the quicksort function.', 'The runtime complexity of the quicksort algorithm is O(log n) in its best and average cases, but can run in O(n^2) in its worst case, which occurs if the array is already sorted or close to being sorted.', 'The space complexity of the quicksort algorithm is O(n) due to recursion, as it uses more space than other sorting algorithms like bubble sort, selection sort, and insertion sort.']}, {'end': 10324.746, 'segs': [{'end': 9786.078, 'src': 'embed', 'start': 9760.45, 'weight': 1, 'content': [{'end': 9764.951, 'text': 'So what do we do? Each of these storage locations is also known as a bucket.', 'start': 9760.45, 'duration': 4.501}, {'end': 9772.694, 'text': 'And the most common resolution for a collision in a hash table is that we will turn each bucket into a linked list.', 'start': 9765.452, 'duration': 7.242}, {'end': 9777.716, 'text': 'If this bucket already has an entry within the first entry,', 'start': 9773.254, 'duration': 4.462}, {'end': 9786.078, 'text': "we will also add an address to the location of the next entry and keep on adding more if there's more entries within this bucket.", 'start': 9777.716, 'duration': 8.362}], 'summary': 'In a hash table, collisions are resolved by turning each bucket into a linked list.', 'duration': 25.628, 'max_score': 9760.45, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s9760450.jpg'}, {'end': 9872.338, 'src': 'embed', 'start': 9846.199, 'weight': 3, 'content': [{'end': 9851.343, 'text': 'But then again, the hash table is going to use up more memory then, so people usually find a balance between the two.', 'start': 9846.199, 'duration': 5.144}, {'end': 9854.265, 'text': 'So yeah, those are hash tables in theory.', 'start': 9852.043, 'duration': 2.222}, {'end': 9855.265, 'text': "Let's create our own now.", 'start': 9854.345, 'duration': 0.92}, {'end': 9859.448, 'text': "Alright everybody, so let's implement a hash table in Java.", 'start': 9855.906, 'duration': 3.542}, {'end': 9865.633, 'text': 'So we will need to declare this hash table and list the data types of our key value pairs.', 'start': 9859.649, 'duration': 5.984}, {'end': 9870.096, 'text': 'If we need to store primitive data types, we can use the appropriate wrapper class.', 'start': 9866.213, 'duration': 3.883}, {'end': 9872.338, 'text': "So let's store integers and strings.", 'start': 9870.396, 'duration': 1.942}], 'summary': 'Balancing memory usage with hash tables in java for storing integers and strings', 'duration': 26.139, 'max_score': 9846.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s9846199.jpg'}, {'end': 9914.962, 'src': 'embed', 'start': 9891.206, 'weight': 2, 'content': [{'end': 9899.591, 'text': 'So in Java, when we create a hash table, these have an initial capacity of 11 elements and a load factor of 0.75.', 'start': 9891.206, 'duration': 8.385}, {'end': 9906.836, 'text': 'So once 75% of our elements are filled, this hash table will dynamically expand to accommodate more elements.', 'start': 9899.591, 'duration': 7.245}, {'end': 9911.78, 'text': "Now you can set a different capacity for your hash table instead of 11, let's say 10,", 'start': 9907.197, 'duration': 4.583}, {'end': 9914.962, 'text': 'to be consistent with our example in the previous part of this lesson.', 'start': 9911.78, 'duration': 3.182}], 'summary': 'In java, hash tables have an initial capacity of 11 elements and a load factor of 0.75, dynamically expanding at 75% fill.', 'duration': 23.756, 'max_score': 9891.206, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s9891206.jpg'}, {'end': 10255.072, 'src': 'embed', 'start': 10221.466, 'weight': 0, 'content': [{'end': 10223.308, 'text': 'These keys are within their own buckets.', 'start': 10221.466, 'duration': 1.842}, {'end': 10231.617, 'text': 'Alright everybody, so in conclusion, a hash table is a data structure that stores unique keys to values.', 'start': 10224.309, 'duration': 7.308}, {'end': 10235.841, 'text': "When you declare a hash table, you state the data types of what you're storing.", 'start': 10231.997, 'duration': 3.844}, {'end': 10238.144, 'text': 'And these are reference data types.', 'start': 10236.262, 'duration': 1.882}, {'end': 10249.109, 'text': 'Each key-value pair is known as an entry, and a benefit of hash tables is that they have a fast insertion, lookup and deletion of key-value pairs,', 'start': 10238.624, 'duration': 10.485}, {'end': 10255.072, 'text': "but they're not ideal for small datasets since there's a lot of overhead, but they're great with large datasets.", 'start': 10249.109, 'duration': 5.963}], 'summary': 'Hash table stores unique keys to values with fast operations, better for large datasets.', 'duration': 33.606, 'max_score': 10221.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10221466.jpg'}, {'end': 10288.895, 'src': 'embed', 'start': 10261.136, 'weight': 4, 'content': [{'end': 10266.14, 'text': 'It utilizes a formula which will vary based on the key as input and its data type.', 'start': 10261.136, 'duration': 5.004}, {'end': 10269.002, 'text': 'Then to calculate an index, we follow this formula.', 'start': 10266.42, 'duration': 2.582}, {'end': 10274.967, 'text': 'We take a hash, modulus, the capacity of our table to calculate an index.', 'start': 10269.422, 'duration': 5.545}, {'end': 10277.609, 'text': 'Each index is also known as a bucket.', 'start': 10275.527, 'duration': 2.082}, {'end': 10281.511, 'text': "It's an indexed storage location for one or more entries.", 'start': 10278.129, 'duration': 3.382}, {'end': 10284.833, 'text': 'They can store more than one entry in the case of a collision.', 'start': 10281.851, 'duration': 2.982}, {'end': 10288.895, 'text': 'And in case that happens, we treat each bucket as a linked list.', 'start': 10285.253, 'duration': 3.642}], 'summary': 'Utilizes formula for indexing by hash, modulus, and capacity. handles collisions with linked lists.', 'duration': 27.759, 'max_score': 10261.136, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10261136.jpg'}], 'start': 9528.251, 'title': 'Hash tables in java and data structure', 'summary': 'Covers the introduction to hash tables in java, including key-value pairs, hashing keys, handling collisions using linked lists, and demonstrates implementation in java. it also explains the concept of hash tables, hash code calculation for different data types, handling collisions, and the benefits and drawbacks of using hash tables for datasets.', 'chapters': [{'end': 10074.326, 'start': 9528.251, 'title': 'Hash tables in java', 'summary': 'Introduces hash tables in java as a collection of key-value pairs, explains the process of hashing keys to find their index, handling collisions using linked lists, and demonstrates implementation in java with examples.', 'duration': 546.075, 'highlights': ['Hash tables are a collection of key-value pairs, each known as an entry, used for storing data. Explains the concept of hash tables as a collection of key-value pairs for data storage.', 'The process of hashing keys involves finding the hash code of a key by plugging it into a formula, dividing it by the capacity of the hash table, and using the remainder as the index. Describes the process of hashing keys, finding the hash code, and using the remainder as the index for placing entries in the hash table.', 'Handling collisions in hash tables is commonly resolved by turning each bucket into a linked list using chaining, which allows searching linearly through the bucket to find the key. Explains the common resolution of handling collisions in hash tables by using chaining to turn each bucket into a linked list for efficient key search.', 'In Java, hash tables have an initial capacity of 11 elements and a load factor of 0.75, dynamically expanding when 75% of the elements are filled. Provides information about the initial capacity and load factor of hash tables in Java, indicating dynamic expansion based on the fill level.', 'Demonstrates the implementation of hash tables in Java using the put method to add key-value pairs and the get method to access values. Demonstrates the practical implementation of hash tables in Java, including adding key-value pairs and accessing values using put and get methods.']}, {'end': 10324.746, 'start': 10074.906, 'title': 'Hash table data structure', 'summary': 'Explains the concept of hash tables, including the calculation of hash codes for different data types, handling collisions, and the benefits and drawbacks of using hash tables for large and small datasets.', 'duration': 249.84, 'highlights': ['A hash table is a data structure that stores unique keys to values. It is highlighted that a hash table is designed to store unique key-value pairs, providing a fast insertion, lookup, and deletion of key-value pairs, making it ideal for large datasets.', 'Explanation of hashing and index calculation for hash tables. The process of hashing, which involves computing an integer based on the input key and its data type, and the subsequent calculation of an index using the hash and modulus operator, is explained.', 'Handling collisions in hash tables using linked lists. The concept of collisions in hash tables is addressed, with emphasis on treating each bucket as a linked list in case of collisions, and the importance of minimizing collisions for efficiency.']}], 'duration': 796.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s9528251.jpg', 'highlights': ['A hash table is a data structure that stores unique keys to values. It is highlighted that a hash table is designed to store unique key-value pairs, providing a fast insertion, lookup, and deletion of key-value pairs, making it ideal for large datasets.', 'Handling collisions in hash tables is commonly resolved by turning each bucket into a linked list using chaining, which allows searching linearly through the bucket to find the key. Explains the common resolution of handling collisions in hash tables by using chaining to turn each bucket into a linked list for efficient key search.', 'In Java, hash tables have an initial capacity of 11 elements and a load factor of 0.75, dynamically expanding when 75% of the elements are filled. Provides information about the initial capacity and load factor of hash tables in Java, indicating dynamic expansion based on the fill level.', 'Demonstrates the implementation of hash tables in Java using the put method to add key-value pairs and the get method to access values. Demonstrates the practical implementation of hash tables in Java, including adding key-value pairs and accessing values using put and get methods.', 'The process of hashing keys involves finding the hash code of a key by plugging it into a formula, dividing it by the capacity of the hash table, and using the remainder as the index. Describes the process of hashing keys, finding the hash code, and using the remainder as the index for placing entries in the hash table.']}, {'end': 11697.846, 'segs': [{'end': 10550.23, 'src': 'embed', 'start': 10524.675, 'weight': 3, 'content': [{'end': 10531.097, 'text': "So the benefits of a matrix is that it's very quick to look up an edge, however a matrix uses a lot of room.", 'start': 10524.675, 'duration': 6.422}, {'end': 10534.118, 'text': 'So it tends to suit graphs that have a lot of edges.', 'start': 10531.497, 'duration': 2.621}, {'end': 10537.5, 'text': 'On the other hand, we have an adjacency list.', 'start': 10534.878, 'duration': 2.622}, {'end': 10542.704, 'text': 'An adjacency list is an array or array list of linked lists.', 'start': 10538, 'duration': 4.704}, {'end': 10550.23, 'text': 'Each element is a separate linked list, and each header within the linked list would contain the address of a node.', 'start': 10543.144, 'duration': 7.086}], 'summary': 'Matrix: quick lookup, uses more space. adjacency list: suitable for graphs with many edges.', 'duration': 25.555, 'max_score': 10524.675, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10524675.jpg'}, {'end': 10641.948, 'src': 'embed', 'start': 10611.222, 'weight': 0, 'content': [{'end': 10615.868, 'text': 'However, a benefit of a list over a matrix is that they use less space.', 'start': 10611.222, 'duration': 4.646}, {'end': 10621.135, 'text': 'The space complexity of an adjacency list is big O of V plus E.', 'start': 10616.229, 'duration': 4.906}, {'end': 10626.021, 'text': 'V for the number of vertices, aka nodes, and E for the number of edges.', 'start': 10621.135, 'duration': 4.886}, {'end': 10628.783, 'text': 'so yeah, everybody, those are graphs.', 'start': 10626.562, 'duration': 2.221}, {'end': 10631.384, 'text': 'a graph can be used to model a network.', 'start': 10628.783, 'duration': 2.601}, {'end': 10635.785, 'text': 'each node is a piece of data within our network and an edge connects nodes.', 'start': 10631.384, 'duration': 4.401}, {'end': 10641.948, 'text': "so, like i said, it's a popular way to model networks which don't necessarily have any sort of order.", 'start': 10635.785, 'duration': 6.163}], 'summary': 'Adjacency lists use less space than matrices. space complexity: o(v + e). graphs model networks with nodes and edges.', 'duration': 30.726, 'max_score': 10611.222, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10611222.jpg'}, {'end': 10699.738, 'src': 'embed', 'start': 10674.96, 'weight': 1, 'content': [{'end': 10681.327, 'text': 'An adjacency matrix is a 2D array to store ones and zeros to represent edges between nodes.', 'start': 10674.96, 'duration': 6.367}, {'end': 10682.968, 'text': 'There are different variations.', 'start': 10681.627, 'duration': 1.341}, {'end': 10684.349, 'text': 'You could use booleans.', 'start': 10683.188, 'duration': 1.161}, {'end': 10688.131, 'text': "So you could say true or false, depending if there's an edge or not.", 'start': 10684.669, 'duration': 3.462}, {'end': 10695.256, 'text': 'And basically the number of rows and columns in an adjacency matrix is equal to the number of unique nodes.', 'start': 10688.351, 'duration': 6.905}, {'end': 10699.738, 'text': "The runtime complexity to check an edge is O It's constant.", 'start': 10695.476, 'duration': 4.262}], 'summary': 'Adjacency matrix stores edges between nodes. runtime complexity to check an edge is o(1).', 'duration': 24.778, 'max_score': 10674.96, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10674960.jpg'}, {'end': 11102.657, 'src': 'heatmap', 'start': 10952.611, 'weight': 0.712, 'content': [{'end': 10957.252, 'text': 'So this first node would have an index of 0, and the second node would have an index of 1.', 'start': 10952.611, 'duration': 4.641}, {'end': 10961.853, 'text': 'If I need an edge between these two, I will add edge between 0 and 1.', 'start': 10957.252, 'duration': 4.601}, {'end': 10962.794, 'text': "And let's create a few others.", 'start': 10961.853, 'duration': 0.941}, {'end': 10971.576, 'text': 'So how about B and C? So C would have an index of 2, and C will have two edges based on the previous video.', 'start': 10963.674, 'duration': 7.902}, {'end': 10981.943, 'text': 'So C will be connected to D, so 2 to 3, as well as E, so 2 and 4.', 'start': 10972.196, 'duration': 9.747}, {'end': 10983.204, 'text': "D won't have any edges.", 'start': 10981.943, 'duration': 1.261}, {'end': 10985.486, 'text': 'This is a directed graph in this example.', 'start': 10983.624, 'duration': 1.862}, {'end': 10987.688, 'text': 'And E has two edges.', 'start': 10986.287, 'duration': 1.401}, {'end': 10989.229, 'text': 'We have E to A.', 'start': 10988.208, 'duration': 1.021}, {'end': 10992.312, 'text': 'That would be 4 to 0.', 'start': 10989.229, 'duration': 3.083}, {'end': 10993.112, 'text': 'And E to C.', 'start': 10992.312, 'duration': 0.8}, {'end': 10995.254, 'text': '4 and 2.', 'start': 10993.132, 'duration': 2.122}, {'end': 10996.255, 'text': "Now let's print our graph.", 'start': 10995.254, 'duration': 1.001}, {'end': 10999.557, 'text': 'Graph.print And we need to fill in this method.', 'start': 10996.575, 'duration': 2.982}, {'end': 11003.701, 'text': 'Within the print method of the graph class, we just have to print our 2D array.', 'start': 11000.078, 'duration': 3.623}, {'end': 11005.602, 'text': 'And we can use for loops for that.', 'start': 11004.241, 'duration': 1.361}, {'end': 11010.676, 'text': 'So this will be the outer for loop, int i equals 0.', 'start': 11006.593, 'duration': 4.083}, {'end': 11016.8, 'text': 'We will continue this as long as i is less than the length of our matrix, matrix.length.', 'start': 11010.676, 'duration': 6.124}, {'end': 11019.282, 'text': 'Then increment i by 1.', 'start': 11017.24, 'duration': 2.042}, {'end': 11022.184, 'text': 'So this will iterate over all of the rows in our matrix.', 'start': 11019.282, 'duration': 2.902}, {'end': 11024.485, 'text': 'And then we need a nested for loop.', 'start': 11022.764, 'duration': 1.721}, {'end': 11027.727, 'text': 'So change i to j within the nested loop.', 'start': 11025.046, 'duration': 2.681}, {'end': 11035.553, 'text': "Whatever index we're on, we will take matrix at index of i.length this time as the stopping condition.", 'start': 11029.228, 'duration': 6.325}, {'end': 11040.78, 'text': 'And during each iteration of the inner for loop, I will use a print statement.', 'start': 11036.934, 'duration': 3.846}, {'end': 11047.99, 'text': 'And I will print matrix at indices of i and j.', 'start': 11041.441, 'duration': 6.549}, {'end': 11049.572, 'text': "Then I'll add a space between each of these.", 'start': 11047.99, 'duration': 1.582}, {'end': 11052.817, 'text': "Oh, then when you exit the inner for loop, let's print a new line.", 'start': 11050.093, 'duration': 2.724}, {'end': 11056.519, 'text': "okay, and here's our adjacency matrix.", 'start': 11053.718, 'duration': 2.801}, {'end': 11060.14, 'text': 'each row corresponds to a node as well as each column.', 'start': 11056.519, 'duration': 3.621}, {'end': 11065.382, 'text': "if there's adjacency between two nodes, well then there will be a one at that row and column.", 'start': 11060.14, 'duration': 5.242}, {'end': 11067.123, 'text': "this next part really isn't necessary.", 'start': 11065.382, 'duration': 1.741}, {'end': 11069.884, 'text': 'this is kind of the general idea of an adjacency matrix.', 'start': 11067.123, 'duration': 2.761}, {'end': 11074.365, 'text': "but let's add some headers to the rows and columns within the graph class at the top.", 'start': 11069.884, 'duration': 4.481}, {'end': 11081.688, 'text': "let's create an array list, array list and the data type will be node, and i will name this nodes.", 'start': 11074.365, 'duration': 7.323}, {'end': 11086.595, 'text': "When we construct a graph object, let's instantiate our nodes array list.", 'start': 11082.83, 'duration': 3.765}, {'end': 11090.901, 'text': 'Nodes equals new array list.', 'start': 11087.757, 'duration': 3.144}, {'end': 11098.472, 'text': 'And when we add a node, we just take our nodes array list dot add node.', 'start': 11093.565, 'duration': 4.907}, {'end': 11102.657, 'text': "And within the print method, let's make a few changes.", 'start': 11100.177, 'duration': 2.48}], 'summary': 'Creating a directed graph with nodes a, b, c, d, and e, and printing its adjacency matrix.', 'duration': 150.046, 'max_score': 10952.611, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10952611.jpg'}], 'start': 10325.286, 'title': 'Graphs & adjacency representation', 'summary': 'Introduces graphs, adjacency matrices, and adjacency lists, emphasizing time and space complexities. it discusses adjacency matrix and lists, with o(1) runtime complexity and o(n^2) space complexity for adjacency matrices, and space complexity of o(v squared) for adjacency lists.', 'chapters': [{'end': 10674.799, 'start': 10325.286, 'title': 'Intro to graphs & adjacency representation', 'summary': 'Introduces the concepts of graphs, including undirected and directed graphs, adjacency matrices, and adjacency lists, and their respective pros and cons, with emphasis on their time and space complexities. it also mentions the use of graphs for network modeling and teases the creation of an adjacency matrix and adjacency list in the next topics.', 'duration': 349.513, 'highlights': ['The chapter introduces the concepts of graphs, including undirected and directed graphs, adjacency matrices, and adjacency lists, and their respective pros and cons. It covers the fundamental concepts of graph theory, including the types of graphs and their representations, highlighting the importance of adjacency matrices and lists in computer science.', 'Emphasizes the time and space complexities of adjacency matrices and lists. It provides a detailed comparison of the time and space complexities of adjacency matrices and lists, showcasing the trade-offs between the two representations in terms of efficiency and storage requirements.', 'Mentions the use of graphs for network modeling and hints at the upcoming creation of an adjacency matrix and adjacency list. It discusses the application of graphs in network modeling and teases the audience with the prospect of creating an adjacency matrix and adjacency list in the subsequent topics, enticing engagement and anticipation.']}, {'end': 10886.5, 'start': 10674.96, 'title': 'Adjacency matrix for graphs', 'summary': 'Discusses the use of adjacency matrix to represent edges between nodes in a graph, with a runtime complexity of o(1) for checking an edge and a space complexity of o(n^2), while also demonstrating the implementation of the adjacency matrix in java.', 'duration': 211.54, 'highlights': ['Adjacency matrix is a 2D array used to represent edges between nodes in a graph, with variations including the use of booleans and a size equal to the number of unique nodes. It stores ones and zeros to represent edges between nodes, with variations including the use of booleans, and the number of rows and columns being equal to the number of unique nodes.', 'Runtime complexity to check an edge is O(1), providing constant time complexity for edge checking. The runtime complexity to check an edge is O(1), ensuring constant time complexity for checking edges.', 'Space complexity is O(n^2), indicating that the adjacency matrix uses a significant amount of space. The space complexity is O(n^2), signifying that the adjacency matrix utilizes a substantial amount of space.', 'Implementation of adjacency matrix is demonstrated in Java using classes for Graph and Node, with methods for adding nodes, adding edges, checking edges, and printing the graph. The implementation of the adjacency matrix is demonstrated in Java using classes for Graph and Node, along with methods for adding nodes, adding edges, checking edges, and printing the graph.']}, {'end': 11250.049, 'start': 10888.536, 'title': 'Adjacency matrix in computer science', 'summary': 'Explains how to create an adjacency matrix in computer science to represent a graph, utilizing nodes and edges, with a space complexity of o(v squared) and a runtime complexity of o(1).', 'duration': 361.513, 'highlights': ['An adjacency matrix in computer science is an array to store ones and zeros to represent edges, with the number of rows and columns equal to the number of unique nodes. It explains that an adjacency matrix in computer science is used as an array to store ones and zeros to represent edges, with the number of rows and columns being equal to the number of unique nodes.', 'The space complexity for an adjacency matrix is O(V squared), where V is the number of nodes, resulting in 25 elements for 5 nodes. It specifies that the space complexity for an adjacency matrix is O(V squared), where V represents the number of nodes, resulting in 25 elements for 5 nodes.', 'The runtime complexity to check an edge is O(1), requiring only two indices for the check. It mentions that the runtime complexity to check an edge is O(1), requiring only two indices for the check.']}, {'end': 11697.846, 'start': 11251.24, 'title': 'Adjacency lists in computer science', 'summary': 'Explains adjacency lists in computer science, which involves representing a graph using an array or array list made up of linked lists, with each linked list containing nodes and unique nodes at the head, and demonstrates the implementation of adjacency lists through creating classes, adding nodes and edges, checking edges, and printing the adjacency list.', 'duration': 446.606, 'highlights': ['Adjacency lists are used to represent a graph using an array or array list made up of linked lists, with each linked list containing nodes and unique nodes at the head. This demonstrates the fundamental concept of adjacency lists in computer science.', 'Demonstrates the implementation of adjacency lists through creating classes, adding nodes and edges, checking edges, and printing the adjacency list. This highlights the practical application of adjacency lists in computer science.', 'The process of adding a node involves creating a new linked list, adding a node to the linked list, and then adding the linked list to the array list. This explains the step-by-step process of adding a node in an adjacency list.']}], 'duration': 1372.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s10325286.jpg', 'highlights': ['The chapter introduces the concepts of graphs, including adjacency matrices and lists, emphasizing their time and space complexities.', 'Adjacency matrix is a 2D array used to represent edges between nodes in a graph, with runtime complexity O(1) and space complexity O(n^2).', 'An adjacency matrix in computer science is an array to store ones and zeros to represent edges, with space complexity O(V squared) and runtime complexity O(1).', 'Adjacency lists are used to represent a graph using an array or array list made up of linked lists, demonstrating practical application.']}, {'end': 12470.953, 'segs': [{'end': 11745.542, 'src': 'embed', 'start': 11720.263, 'weight': 0, 'content': [{'end': 11725.086, 'text': "And all adjacent neighbors to that node are added to the node's linked list at the tail.", 'start': 11720.263, 'duration': 4.823}, {'end': 11730.49, 'text': 'The runtime complexity to check an edge is O v for the number of vertices.', 'start': 11725.647, 'duration': 4.843}, {'end': 11735.093, 'text': "It's because we need to traverse a linked list linearly to find a matching node.", 'start': 11730.99, 'duration': 4.103}, {'end': 11742.899, 'text': 'And the space complexity for an adjacency list is O v as in the number of vertices, e as in the number of edges.', 'start': 11735.473, 'duration': 7.426}, {'end': 11745.542, 'text': "So yeah, that's an adjacency list.", 'start': 11743.599, 'duration': 1.943}], 'summary': 'Adjacency list has o(v) runtime and o(v+e) space complexity.', 'duration': 25.279, 'max_score': 11720.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s11720263.jpg'}, {'end': 12248.212, 'src': 'embed', 'start': 12223.57, 'weight': 2, 'content': [{'end': 12229.659, 'text': 'Breadth-first search is a search algorithm for traversing a tree or graph data structure.', 'start': 12223.57, 'duration': 6.089}, {'end': 12236.448, 'text': 'This is done one level at a time, rather than one branch at a time, like what you see with depth-first search.', 'start': 12229.999, 'duration': 6.449}, {'end': 12237.509, 'text': "Here's an example.", 'start': 12236.808, 'duration': 0.701}, {'end': 12243.511, 'text': 'In the previous topic on depth first search, we would navigate a graph one branch at a time.', 'start': 12238.05, 'duration': 5.461}, {'end': 12248.212, 'text': 'But in a breadth first search approach, we would navigate this graph one level at a time.', 'start': 12243.851, 'duration': 4.361}], 'summary': 'Breadth-first search traverses tree/graph data one level at a time.', 'duration': 24.642, 'max_score': 12223.57, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s12223570.jpg'}], 'start': 11697.846, 'title': 'Adjacency list data structure, dfs, and bfs', 'summary': 'Explains the adjacency list data structure using linked lists, with runtime complexity for edge checking as o(v) and space complexity as o(v+e). it also covers depth-first and breadth-first search algorithms, detailing their implementation, traversal approaches, and differences in data structures used.', 'chapters': [{'end': 11745.542, 'start': 11697.846, 'title': 'Adjacency list data structure', 'summary': 'Explains the concept of an adjacency list data structure, where it is defined as an array or array list made up of linked lists, each containing a unique node at the head and all adjacent neighbors at the tail. it highlights the runtime complexity for checking an edge as o(v) and the space complexity as o(v+e).', 'duration': 47.696, 'highlights': ["An adjacency list is an array or array list made up of linked lists, where each linked list has a unique node at the head and all adjacent neighbors to that node are added to the node's linked list at the tail.", 'The runtime complexity to check an edge is O(v) for the number of vertices, as it requires linear traversal of the linked list to find a matching node.', 'The space complexity for an adjacency list is O(v+e), where v represents the number of vertices and e represents the number of edges.']}, {'end': 12470.953, 'start': 11745.802, 'title': 'Depth-first and breadth-first search', 'summary': 'Covers the concepts of depth-first search and breadth-first search algorithms, including their implementation in code and traversal approaches. it explains the steps involved in depth-first search, utilizing a stack for tracking node traversal, and then delves into breadth-first search, utilizing a queue for traversal. the chapter provides examples and code implementation for both search algorithms, emphasizing the differences between their traversal approaches and data structures used.', 'duration': 725.151, 'highlights': ['The chapter covers the concepts of depth-first search and breadth-first search algorithms, including their implementation in code and traversal approaches. The chapter introduces and discusses the concepts of depth-first search and breadth-first search algorithms, along with their implementation and traversal approaches.', 'It explains the steps involved in depth-first search, utilizing a stack for tracking node traversal, and then delves into breadth-first search, utilizing a queue for traversal. The explanation of depth-first search includes the utilization of a stack for node traversal, while breadth-first search involves the use of a queue for traversal.', 'The chapter provides examples and code implementation for both search algorithms, emphasizing the differences between their traversal approaches and data structures used. Examples and code implementation for both search algorithms are provided, highlighting the distinctions in their traversal approaches and utilized data structures.']}], 'duration': 773.107, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s11697846.jpg', 'highlights': ['The space complexity for an adjacency list is O(v+e), where v represents the number of vertices and e represents the number of edges.', 'The runtime complexity to check an edge is O(v) for the number of vertices, as it requires linear traversal of the linked list to find a matching node.', 'The chapter provides examples and code implementation for both search algorithms, emphasizing the differences between their traversal approaches and data structures used.', 'The chapter covers the concepts of depth-first search and breadth-first search algorithms, including their implementation in code and traversal approaches.']}, {'end': 14379.729, 'segs': [{'end': 12838.38, 'src': 'embed', 'start': 12809.061, 'weight': 1, 'content': [{'end': 12810.401, 'text': "that's why it's binary.", 'start': 12809.061, 'duration': 1.34}, {'end': 12816.663, 'text': 'in this example, node one has two children, two and three, and each of these children have their own children.', 'start': 12810.401, 'duration': 6.262}, {'end': 12819.005, 'text': 'They have no more than two children.', 'start': 12816.924, 'duration': 2.081}, {'end': 12824.668, 'text': 'Nodes 4 and 5 are the children of 2, and nodes 6 and 7 are the children of 3.', 'start': 12819.285, 'duration': 5.383}, {'end': 12827.969, 'text': 'In this similar example, this would also be a binary tree.', 'start': 12824.668, 'duration': 3.301}, {'end': 12831.371, 'text': 'Node 3 only has one child, node 6.', 'start': 12828.369, 'duration': 3.002}, {'end': 12835.536, 'text': 'But with a binary tree, no node has more than two children.', 'start': 12831.371, 'duration': 4.165}, {'end': 12838.38, 'text': "Since node three has one child, that's fine.", 'start': 12835.997, 'duration': 2.383}], 'summary': 'Binary tree with a maximum of 2 children per node.', 'duration': 29.319, 'max_score': 12809.061, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s12809061.jpg'}, {'end': 12876.329, 'src': 'embed', 'start': 12852.196, 'weight': 2, 'content': [{'end': 12858.079, 'text': 'The root node should be greater than the left child but less than the right child.', 'start': 12852.196, 'duration': 5.883}, {'end': 12861.881, 'text': '4 is greater than 2 and 4 is less than 6.', 'start': 12858.099, 'duration': 3.782}, {'end': 12864.623, 'text': "That's the pattern that this data structure is built around.", 'start': 12861.881, 'duration': 2.742}, {'end': 12868.265, 'text': "If we take a look at some of the subtrees, let's say this one.", 'start': 12864.923, 'duration': 3.342}, {'end': 12871.466, 'text': '2 is greater than 1 but less than 3.', 'start': 12868.285, 'duration': 3.181}, {'end': 12876.329, 'text': 'And in this subtree, 6 is greater than 5 but less than 7.', 'start': 12871.466, 'duration': 4.863}], 'summary': 'Data structure requires root > left & root < right. 4 > 2 and 4 < 6, following pattern. subtrees: 2 > 1 & 2 < 3, 6 > 5 & 6 < 7.', 'duration': 24.133, 'max_score': 12852.196, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s12852196.jpg'}, {'end': 13424.174, 'src': 'embed', 'start': 13399.171, 'weight': 3, 'content': [{'end': 13406.558, 'text': 'System.out.println In our first subtree, the data all the way to the left is 1.', 'start': 13399.171, 'duration': 7.387}, {'end': 13409.1, 'text': 'And the root node of that subtree is 2.', 'start': 13406.558, 'duration': 2.542}, {'end': 13412.343, 'text': 'Then we need the right child, which should theoretically be 3.', 'start': 13409.1, 'duration': 3.243}, {'end': 13417.028, 'text': "So again, we'll invoke the display helper method and pass in the right child.", 'start': 13412.343, 'duration': 4.685}, {'end': 13419.45, 'text': 'So this is in order traversal.', 'start': 13417.408, 'duration': 2.042}, {'end': 13423.033, 'text': 'All of the nodes will be displayed in non-decreasing order.', 'start': 13419.911, 'duration': 3.122}, {'end': 13424.174, 'text': "So let's try this.", 'start': 13423.474, 'duration': 0.7}], 'summary': 'In-order traversal displays nodes in non-decreasing order. subtree: 1 left, 2 root, 3 right.', 'duration': 25.003, 'max_score': 13399.171, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s13399171.jpg'}, {'end': 13892.551, 'src': 'embed', 'start': 13861.778, 'weight': 0, 'content': [{'end': 13866.501, 'text': 'While this condition is true, take root, set this equal to root.left.', 'start': 13861.778, 'duration': 4.723}, {'end': 13872.198, 'text': 'and at the end return root.data.', 'start': 13868.976, 'duration': 3.222}, {'end': 13876.621, 'text': "So what's happening here within the remove helper method?", 'start': 13873.179, 'duration': 3.442}, {'end': 13882.605, 'text': "if the node we're trying to delete has a right child, we need to find a successor to fill in that gap.", 'start': 13876.621, 'duration': 5.984}, {'end': 13885.807, 'text': 'And that successor should have the least value.', 'start': 13883.125, 'duration': 2.682}, {'end': 13892.551, 'text': 'So we will go right and look as far left as we can because values on the left are less than the root.', 'start': 13886.647, 'duration': 5.904}], 'summary': 'Remove method finds successor with least value for right child deletion.', 'duration': 30.773, 'max_score': 13861.778, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s13861778.jpg'}, {'end': 14051.872, 'src': 'embed', 'start': 14026.238, 'weight': 4, 'content': [{'end': 14032.141, 'text': "We don't have random access, so we begin at the root node and follow a certain procedure to visit all of the nodes of a tree.", 'start': 14026.238, 'duration': 5.903}, {'end': 14034.262, 'text': "And there's three that we're going to talk about.", 'start': 14032.641, 'duration': 1.621}, {'end': 14037.584, 'text': 'In-order, post-order, and pre-order traversal.', 'start': 14034.763, 'duration': 2.821}, {'end': 14046.649, 'text': 'In this example, we will be working with a binary tree, not a binary search tree, because these values are not in binary search tree order.', 'start': 14038.084, 'duration': 8.565}, {'end': 14051.872, 'text': 'So each node has at most two children, a left node and a right node.', 'start': 14047.169, 'duration': 4.703}], 'summary': 'Explaining tree traversal methods: in-order, post-order, and pre-order.', 'duration': 25.634, 'max_score': 14026.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s14026238.jpg'}], 'start': 12471.594, 'title': 'Binary search trees', 'summary': 'Introduces binary search trees and their implementation, covering tree terminology, binary search tree methods, and traversal techniques, with examples and a 50% success rate for binary search tree methods.', 'chapters': [{'end': 12618.752, 'start': 12471.594, 'title': 'Breadth first search', 'summary': 'Explains the implementation of breadth-first search algorithm, illustrating its process and differences from depth-first search. it provides an example of visiting nodes in a graph using breadth-first search starting from node a, and highlights the differences between breadth and depth first searches.', 'duration': 147.158, 'highlights': ['Breadth traverses a graph level by level. Depth traverses a graph branch by branch. Breadth utilizes a queue. Depth utilizes a stack. Breadth tends to be better if the destination is on average close to the start. And depth tends to be better if the destination is on average far from the start. In a breadth-first search, siblings are visited before children. In a depth-first search, children are visited before siblings. If you ever plan on creating video games, depth-first searches tend to be more popular than breadth-first searches.', "In this example, node a has an index of zero, b is one, c is two, so on and so forth. So let's perform a breadth-first search, beginning at node a. We will cover these nodes in this order. A, B, C, E, D.", "We can't go anywhere from node D. So only D is visited. And E. E-A-C-B-D."]}, {'end': 12922.614, 'start': 12620.826, 'title': 'Introduction to trees and binary search trees', 'summary': 'Introduces the concept of trees, covering terminology like nodes, edges, root node, leaf nodes, depth, and height, and then delves into binary search trees, emphasizing the arrangement of values and the quick lookup for data retrieval with a runtime complexity of o(log n).', 'duration': 301.788, 'highlights': ['A tree is a nonlinear data structure where nodes are organized in a hierarchy, used in file explorers, databases, domain name servers, and the HTML document object model. It explains the concept of trees and their real-life applications in programming and technology.', 'The root node, leaf nodes, branch nodes, parents, children, siblings, and subtree are explained, along with the size, depth, and height of a tree. Covers the fundamental terminology and characteristics of a tree, providing a comprehensive understanding of its structure and properties.', 'Binary search trees are introduced, emphasizing the arrangement of values and the quick lookup for data retrieval with a runtime complexity of O(log n). Introduces the concept of binary search trees, focusing on the organization of values and the efficient search operations with a low runtime complexity.']}, {'end': 13451.75, 'start': 12922.714, 'title': 'Binary search tree implementation', 'summary': 'Discusses the implementation of a binary search tree, including the creation of node and binary search tree classes, insertion of nodes, display of nodes in ascending order, and the use of recursion for traversal, culminating in the display of the binary search tree.', 'duration': 529.036, 'highlights': ['Creation of Node and Binary Search Tree Classes The chapter explains the creation of a node class to hold data and child nodes, as well as the implementation of a binary search tree class with a root node.', 'Insertion of Nodes Using Recursion The chapter details the insertion of nodes using recursion, where each node is compared with the root node and placed as a left or right child accordingly, leading to the construction of the binary search tree.', 'Displaying Nodes in Ascending Order The chapter demonstrates the use of in-order traversal and recursion to display the nodes of the binary search tree in ascending order, thereby showcasing the functionality of the display method.', 'Utilizing Recursion for Traversal The chapter emphasizes the use of recursion for traversal within the binary search tree, illustrating its application in displaying nodes in ascending order and providing a clear understanding of the traversal process.']}, {'end': 13961.402, 'start': 13452.99, 'title': 'Binary search tree methods', 'summary': 'Covers search, remove, successor, and predecessor methods in a binary search tree, including examples of searching for data and removing nodes, with a success rate of 50% in the provided examples.', 'duration': 508.412, 'highlights': ['The search method checks if the data exists in the tree and returns true if found, with examples of successful searches for 1 and 9, and unsuccessful search for 0 and 10. 5', 'The remove method first invokes the search method to check if the data exists, then uses a remove helper method to handle the removal process, with successful examples of removing nodes 1, 5, and 9, and an unsuccessful attempt to remove node 0. 4', 'The successor and predecessor methods are used to find the least and greatest values below the right and left child nodes, respectively, to fill in gaps when deleting nodes with children. 3']}, {'end': 14379.729, 'start': 13962.203, 'title': 'Tree traversal and binary search trees', 'summary': 'Discusses the concept of binary search trees, their benefits, runtime complexity, and the process of tree traversal, including in-order, post-order, and pre-order traversal. it also explains how to calculate the runtime of a program and the importance of tree traversal in computer science.', 'duration': 417.526, 'highlights': ['Binary search trees follow specific rules, but can become unbalanced, affecting the runtime complexity, with a best case scenario of O and a worst case scenario of O depending on balance. The runtime complexity of binary search trees can vary from O to O based on their balance, affecting the efficiency of operations.', 'In-order traversal involves visiting all nodes in non-decreasing order, while post-order traversal is used to delete a tree from leaf to root, and pre-order traversal is used to create a copy of a tree. The three types of tree traversals - in-order, post-order, and pre-order - serve different purposes, such as visiting nodes in a specific order, deleting a tree, or creating a copy of a tree.', 'The process of calculating the runtime of a program involves obtaining the start time at the entry point and determining the duration at the end, which can be measured in different units such as milliseconds or seconds. Calculating the runtime of a program requires capturing the start time at the beginning and determining the duration at the end, which can be expressed in various units of time.']}], 'duration': 1908.135, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CBYHwZcbD-s/pics/CBYHwZcbD-s12471594.jpg', 'highlights': ['Binary search trees are introduced, emphasizing the arrangement of values and the quick lookup for data retrieval with a runtime complexity of O(log n).', 'The chapter explains the creation of a node class to hold data and child nodes, as well as the implementation of a binary search tree class with a root node.', 'The chapter details the insertion of nodes using recursion, where each node is compared with the root node and placed as a left or right child accordingly, leading to the construction of the binary search tree.', 'The search method checks if the data exists in the tree and returns true if found, with examples of successful searches for 1 and 9, and unsuccessful search for 0 and 10.', 'In-order traversal involves visiting all nodes in non-decreasing order, while post-order traversal is used to delete a tree from leaf to root, and pre-order traversal is used to create a copy of a tree.']}], 'highlights': ['The chapter emphasizes the importance of learning data structures and algorithms for writing time and memory-efficient code, as well as their relevance to coding job interviews.', 'Practical applications of stacks include undo/redo features in text editors, browser history navigation, backtracking algorithms, and function calls.', 'The push method adds objects to the top of the stack, while the pop method removes objects from the top.', 'Linked lists excel at insertion and deletion of nodes, as it only requires a few steps regardless of the data set size, making it a constant time operation.', 'Dynamic arrays allow for random access of elements in O(1) constant time and have good locality of reference and data cache utilization due to contiguous memory addresses. Dynamic arrays provide efficient random access and utilize memory effectively due to contiguous memory addresses.', 'Big O notation describes algorithm performance as data increases, with examples of O linear time and O constant time.', 'The insertion sort algorithm has a runtime complexity of O(n^2).', 'Merge sort has a runtime complexity of O log n, making it efficient for large data sets.', 'A hash table is a data structure that stores unique keys to values. It is highlighted that a hash table is designed to store unique key-value pairs, providing a fast insertion, lookup, and deletion of key-value pairs, making it ideal for large datasets.', 'The chapter introduces the concepts of graphs, including adjacency matrices and lists, emphasizing their time and space complexities.', 'Binary search trees are introduced, emphasizing the arrangement of values and the quick lookup for data retrieval with a runtime complexity of O(log n).']}