Introduction
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 lesson, we'll cover the fundamentals of data structures and
algorithms, including big O notation, arrays, and linked lists.
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.
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
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 lesson, we'll cover the fundamentals of data structures and
algorithms, including big O notation, arrays, and linked lists.
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.
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
