Bianca demonstrates how the linked list is structured and explains how a doubly-linked list may be beneficial over a singly-linked list. Answering a question from a student, Bianca shows what a doubly-linked list would look like in pseudocode.
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After discussing the benefits and disadvantages of a hash table data structure, Bianca explains how a hash table structured, with notes on its time complexity, and its applications. Then Bianca discusses arrays and strings in the context of data structures. In this exercise, students draw a hash table, array, and string data structures from memory. Bianca discusses the implementation and application of the data structures. She brings up the importance of considering both time and space complexity in the code. She then starts the exercises by explaining how to find the prompts, how the constructor method works, the private variable's constraints within the constructor, and the push , pop , and peek methods, as well as general expectations for the stack class exercise.
Bianca takes questions from students about how best to approach the exercise questions, the overall scope of the course, and more. After explaining the process of live coding the solutions, Bianca defines the logic behind the push method in the stack data structure. After defining the logic behind the pop method, Bianca implements the constructor , push , and pop methods. Bianca discusses edge cases to consider in the stack data structure.
Bianca then revisits an earlier point regarding type checking when pushing to the stack and what to do if pop is invoked, but the stack is empty. Bianca reviews the peek method and then discusses other methods to consider such as a method that retrieves the length of the stack.
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Bianca answers questions from students about the challenges with the implementation of the stack data structure and the usage of private variables. Bianca reviews the queue exercise prompt, including the constructor , enqueue , dequeue , peek , and expectations for implementing the Queue class.
Bianca defines the expected outcomes from the methods in the queue data structure and discusses considerations when implementing the code. Bianca addresses questions about the applications for stacks and queues, as well as the importance of considering space complexity. Bianca explains the linked list exercise prompt, including the constructor , insert , remove , contains , and expectations for implementing the LinkedList class.
Then, Bianca discusses the private variable's usage and constraints. Bianca defines the expected outcomes from the methods in the linked list data structure and implements the constructor method. Taking questions from students, Bianca clarifies how the linked list is structured using an interactive diagram and reviews alternative ways that the linked list might be implemented.
Taking questions from students, Bianca discusses considerations in the linked list when writing functions outside of the class, expectations for interview questions regarding linked lists, and more. Bianca reviews tips and tricks for learning critical concepts about a linked list from the students. In doing so, you are following the linear search algorithm. The process continues until the spot where the input is split becomes the item that is being searched.
Binary search is basically just a highly disciplined approach to the process of elimination. An example should make this more clear. What can you do? Enter binary search. You select the person in the very middle of the line, and measure their height.
So right away you know that this person, along with everyone to their right, is not Bin. So you can rule out that person and anyone to their left, cutting the problem in half again. And so on. After just five or six of these splits, you quickly home in on Bin in a fraction of the time it took you to find Lin.
In doing so, you have followed the binary search algorithm.
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Here we look at two of the most useful sorting algorithms: MergeSort and QuickSort. You first have the group of people, which are all huddled together, divide into two. Then you have each of the two groups divide into two again, and so on, until you are dealing entirely with individuals. You then begin to pair the individuals up, and have the taller of the two in each pair stand to the right of the other one. Pretty soon everyone is organized into these left-right ordered pairs. Next, you start merging the ordered pairs into ordered groups of four; then merging the ordered groups of four into ordered groups of eight; and so on.
Finally, you find that you have a complete, height-ordered line of people, just like the one you encountered above. Without knowing it, you have followed the MergeSort algorithm to accomplish your feat.
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You could use MergeSort, but you hear that QuickSort is generally faster, so you decide to try it. Your first step is to choose a number in the list called the pivot. Choosing the right pivot number will make all the difference in how fast your QuickSort performs, and there are ready-made formulas to follow for choosing good pivots. Moving from left to right through our array, our goal is to put any number smaller than the pivot 9 to the left of the separator, and any number greater than or equal to the pivot to the right of the separator.
We start with the first number in our array: 4. To move it to the left of the separator, we just move the separator up one element:. We do the same with the next number: 2. But then we get to 13, which is larger than the pivot number 9 and already to the right side of the separator.
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So we leave it alone. Next we come to 6, which needs to go to the left of the separator. So first, we swap it with 13 to get it into position:. Then we move the separator passed it:. Next up is 15, which is already to the right of the separator, so we leave it alone. Then we have Same thing. But 7, our last number before reaching the pivot, needs the same kind of help moving as 6 did. So we swap 7 with 13 to get it into position:. Then, once again, we move the separator passed it:.
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Finally, we come to our pivot number: 9. Following the same logic as we have above, we swap 15 with 9 to get the pivot number where it needs to be:. The end result will be the following ordered list that took us less time to generate than it would have with the simpler MergeSort:. These are the most common sorting algorithms with some recommendations for when to use them. Internalize these!