Data Structures and Algorithms for Beginners
Programming with Mosh
Introduction
Hey there, it's Mosh! Today, we'll be discussing the basics of data structures and algorithms,
a topic that comes up frequently in coding interviews. Many companies ask questions about
data structures and algorithms to test whether or not you can think like a programmer.
In this video, we'll cover the fundamentals of data structures and algorithms, including big O
notation, arrays, and linked lists. If you're interested in learning more after watching this
video, I encourage you to enroll in my ultimate data structures and algorithms course (link
below).
No prior knowledge of data structures and algorithms is required to watch this video, but
basic programming knowledge is necessary. I'll be using Java in this tutorial, but feel free to
code in your preferred language.
If you enjoy this tutorial, please support me by liking and sharing it with others. Also, be sure
to subscribe, as I regularly upload new videos.
Big O Notation
Before we dive into data structures and algorithms, we need to discuss big O notation. We
use big O notation to describe the performance of an algorithm, which helps us determine if
it's scalable or not. Essentially, we want to know if an algorithm will perform well as the
input grows larger.
Just because your code runs quickly on your computer doesn't mean it will perform well with
a large dataset. Therefore, it's essential to use big O notation to describe an algorithm's
performance.
While big O notation may seem intimidating, it's not as complicated as it appears. In essence,
big O notation describes the limiting behavior of a function as the argument approaches a
specific value or infinity.
Data Structures
As you'll learn in this course, certain operations can be more or less costly depending on the
data structure used. For example, accessing an array element by its index is fast, but arrays
have a fixed length. If you need to constantly add or remove items from an array, it must be
resized, which becomes costly as the input size grows larger.
Other data structures, such as linked lists, can handle dynamic sizes more efficiently.
Therefore, it's essential to choose the appropriate data structure for the task at hand.
Programming with Mosh
Introduction
Hey there, it's Mosh! Today, we'll be discussing the basics of data structures and algorithms,
a topic that comes up frequently in coding interviews. Many companies ask questions about
data structures and algorithms to test whether or not you can think like a programmer.
In this video, we'll cover the fundamentals of data structures and algorithms, including big O
notation, arrays, and linked lists. If you're interested in learning more after watching this
video, I encourage you to enroll in my ultimate data structures and algorithms course (link
below).
No prior knowledge of data structures and algorithms is required to watch this video, but
basic programming knowledge is necessary. I'll be using Java in this tutorial, but feel free to
code in your preferred language.
If you enjoy this tutorial, please support me by liking and sharing it with others. Also, be sure
to subscribe, as I regularly upload new videos.
Big O Notation
Before we dive into data structures and algorithms, we need to discuss big O notation. We
use big O notation to describe the performance of an algorithm, which helps us determine if
it's scalable or not. Essentially, we want to know if an algorithm will perform well as the
input grows larger.
Just because your code runs quickly on your computer doesn't mean it will perform well with
a large dataset. Therefore, it's essential to use big O notation to describe an algorithm's
performance.
While big O notation may seem intimidating, it's not as complicated as it appears. In essence,
big O notation describes the limiting behavior of a function as the argument approaches a
specific value or infinity.
Data Structures
As you'll learn in this course, certain operations can be more or less costly depending on the
data structure used. For example, accessing an array element by its index is fast, but arrays
have a fixed length. If you need to constantly add or remove items from an array, it must be
resized, which becomes costly as the input size grows larger.
Other data structures, such as linked lists, can handle dynamic sizes more efficiently.
Therefore, it's essential to choose the appropriate data structure for the task at hand.
