Gain insights into array and linked list-based implementations, delve into advanced structures like skiplists and trees, and explore template-based collections for efficient data optimization and retrieval.

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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 C++

An implementation of a data structure includes the operations supported by the 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 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.

In the w-bit word-RAM model, which is *true* regarding the running time guarantees?

In the w-bit word-RAM model, which is true regarding the running time guarantees?

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 $w$ $≥$ $log\ n$.

Which statement is *true* about the w–bit word–RAM model?

Which statement is true about the 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