Gain insights into implementing array-based and linked list data structures, explore advanced structures like skiplists, hashing, and graphs, and learn reusable, template-based collections for efficient data analysis.

java.tar.gz

Java

Java-1

Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in Java

An implementation of a data structure includes the operations supported by that data structure and its semantics.

An interface defines the internal representation of the data structure.

The operations supported by the `Queue` interface are `add(x)` and `add(y)`.

A `Deque` can be implemented using only `addLast(x)` and `removeLast()`.

Which statement is *true* about data structures?

Which statement is true about data structures?

It gives the exact running time of an operation on a computer.

It is a set of functions that starts to dominate a function `g(n)` when `n` is sufficiently large.

It does not apply to the analysis of data structures.

Which statement is *true* regarding asymptotic notation?

Which statement is true regarding asymptotic notation?

Randomized algorithms always produce correct results.

The output of a randomized algorithm may vary on different runs, but its expected behavior is well-defined.

The output of a randomized algorithm is entirely unpredictable and cannot be analyzed.

Randomized algorithms are not useful in practice, since they are only theoretical constructs.

Which statement about randomization and probability in computer science is *true*?

Which statement about randomization and probability in computer science is true?

Operations have worst-case running times.

What is *true* regarding the running time guarantees in the $w$-bit word-RAM model

What is true regarding the running time guarantees in the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>w</mi></mrow><annotation encoding="application/x-tex">w</annotation></semantics></math>w-bit word-RAM model

In this model, we have access to a random access memory consisting of cells, each of which stores a single bit.

Basic operations on words take linear time in this model.

Allocating a block of memory of size k takes $O(log k)$ time in this model.

For any of our data structures storing $n$ elements, the lower bound for word size $w$ must satisfy the condition                   $w\geq\log n$.

What is *true* about the $w$–bit word–RAM model?

What is true about the <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>w</mi></mrow><annotation encoding="application/x-tex">w</annotation></semantics></math>w–bit word–RAM model?

Test yourself on what you've learned so far.

Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

Quiz