For the self-taught, career changers, hobbyist, or other non-CS grad, the world of algorithms, data structures, and efficiency can be so intimidating that real study is avoided. After all, it's definitely possible to get programs working without any meaningful knowledge of algorithmic efficiency. Additionally, it's not the easiest stuff to learn, especially if you're able to get the problems solved your own way.
We'll look at making a case for investing time into some extra study; however, let's look a bit more at what exactly we're discussing.
What do you mean by the term: data structures?
In computer science, a data structure is a way of organizing and storing data in a computer's memory so that it can be accessed and modified efficiently. Some examples of common data structures include the following:
- Arrays: A collection of items stored in contiguous memory locations, each with a specific index. Arrays are efficient for accessing individual elements by index, but not as efficient for inserting or deleting items.
- Linked Lists: A collection of items where each item points to the next item in the list. Linked lists are efficient for inserting and deleting items, but not as efficient for accessing individual elements by index.
- Stacks: A last-in, first-out (LIFO) data structure where elements are added and removed from the top of the stack.
- Queues: A first-in, first-out (FIFO) data structure where elements are added to the back of the queue and removed from the front.
- Trees: A hierarchical data structure where each node has zero or more child nodes. Trees are used for efficient searching and sorting.
- Graphs: A collection of nodes and edges that are used to represent relationships between items. Graphs are often used in networking and routing algorithms.
- Hash tables: A data structure that uses a hash function to map keys to values, allowing for fast lookups, insertions and deletions.
Undoubtedly, anyone writing code is using any number of these structures in their work. The key to this discussion, however, is how we use data structures in algorithms.
So, what really is an algorithm, and why is efficiency important?
Algorithms are a set of instructions that can be followed to solve a specific problem. They are used in everything from web development and mobile apps to artificial intelligence and machine learning. Efficient algorithms are particularly important in situations where the amount of data is large and the time available to process it is limited.
One of the key benefits of efficient algorithms is that they can significantly improve the performance of a computer program. For example, using a more efficient algorithm can lead to faster performance, and the ability to handle larger amounts of data. This can be especially important in fields such as finance, healthcare, and transportation, where the ability to process large amounts of data quickly can have a direct impact on people's lives.
Efficient algorithms can also help to reduce costs. For example, using a more efficient algorithm can lead to a reduction in the number of servers or other hardware needed to run a program, which in turn can lead to cost savings.
In summation, efficient algorithms are important because they can significantly improve the performance of a computer program, handle larger amounts of data and reduce costs. Whether you're a software engineer, data scientist, or working in another field that relies on technology, understanding how to design and implement efficient algorithms is a valuable skill to have.
As someone with an interest of a practice in programming, you're clearly not afraid of a challenge. That's a big deal, as it often means that you actually enjoy a challenge. Investing time into learning how to construct efficient algorithms does not only involve study, but it requires a ton of practice. The practice is fun!
To practice algorithms, we solve word problems in the programming language of our choice. Now's a good time to jump ship on this article and go have a look at some great resources.