Whether we realize it or not, algorithms are a big part of our daily lives. Not only are they in the most mundane tasks. Also, they’re part of any device we put our hands on. But what exactly are they? How do they work? Which ones should you remember if you want to dive into computer programming? In this article, we take a look at this and more!

An algorithm is a set of instructions followed to complete a task or solve a problem. Although they may sound like a complex concept, we use algorithms daily. For instance, when we follow a recipe or solve a long division. When it comes to computer science, the one reading the algorithm is the computer. In this case, algorithms are like very detailed recipes or instruction manuals. As a result, computers read them to function as we’d like them to.

Data structures are a way of organizing and storing data in a virtual system. As a result, this data gets efficient uses. These shared data structures include arrays, stacks, and queues. Why are we mentioning data structures here? Well, algorithms solve problems by processing data. Thus, they often combine with algorithms. To do so, it optimizes code and builds computer programs.

In the context of computing, algorithms work based on input and output. An algorithm provides a computer with the input it needs to carry out the appropriate steps. After that, it outputs the results.

Algorithms come in a variety of forms, shapes, and sizes!

This algorithm is quite essential as it's used to arrange the order of an array or list of elements. Sorting algorithms’ classification is into integer sorts and comparison sorts. Integer sorts —or counting sorts— determine how many factors in the array are less than X for any X element. Meanwhile, comparison sorts compare features in the array. Consequently, these determine if a component should be at the left or right of another. Some popular sorting algorithms include **bubble sort**, **merge sort**, and **heap sort**.

These algorithms divide the problem into subproblems of the same type. After solving —or conquering— each of them is combined to solve the original problem. This type of algorithm often applies to **binary search** and **matrix multiplication**. Some algorithms of this logic include quicksort and the **closest pair of points**.

The concept behind these algorithms is simple: finding an element in a data set. Searching algorithms get divided into sequential and interval searches. Some search algorithms include **binary search**, **breadth-first search**, and depth-first search.

The idea behind these algorithms is also quite simple. Brute Force iterates all possible solutions until they find a satisfactory one. You can think about trying every possible combination to open a safe.

This computer algorithm divides the problem into subproblems like *divide and conquer*. Only one subproblem gets solved at a time. If a solution fails, the last step is undone. Then, the algorithm backtracks to find another solution. Backtracking builds incremental solutions and cuts solutions unaligned with the problem. You can use backtracking algorithms to solve **the Knight’s tour problem** or a **Sudoku game**. Also, it’s popular for **all binary strings** and the **knapsack problem**.

Here, the simplest version of a problem gets solved first. Then, it moves on to larger versions of the problem until you find the solution to the original problem. There are different types of recursion. There is direct, indirect, tailed, and non-tailed recursion among them. Further, it's a good type to solve the *Tower of Hanoi *problem or the **DFS of a graph**.

This algorithm is a very efficient way to solve problems. Like other algorithms in this list, the main problem gets divided into simpler subproblems. Each gets a fix once, and the solution to the bigger problem is then computed. Yet, the answers to the subproblems are in storage for future use. Among its uses are **matrix chain multiplication** and the **longest common subsequence**.

These computer algorithms make a decision based on random numbers. Here, the probability is the most crucial aspect. Randomized algorithms get classified into Las Vegas and Monte Carlo algorithms. The first always produces optimal or correct results. Meanwhile, the latter produces accurate results with some probability. Randomized algorithms apply for **load balancing** and **rumor blocking in social media**.

The first step in a greedy algorithm is finding an optimal local solution. With this, it hopes to find an optimal global solution. This algorithmic paradigm builds on the solution piece by piece. To do so, it chooses the piece that offers the most immediate benefit first. Because no future considerations are involved, this algorithm does not always work. But when it does, it's great to solve optimization problems. It's applied for **sorting**, **job scheduling**, and the **coin change problem**.

Algorithms must follow specific rules to be good or efficient, like anything code-related. Though it might sound obvious, the first thing to consider when it comes to a suitable algorithm is for it to be correct. This means it must have the correct output for each set of inputs. Moreover, both the input and the output need proper specifications. These rules encompass all its relevant details. Algorithms should also be time and memory efficient. Further, they should have a finite number of steps and be easy to understand and install.

Since they're the backbone of Computer Science, algorithms are everywhere you look. After all, it is through algorithms that computers function. Computer programs are pretty much algorithms written in a language that machines understand. Also, they play a significant role in the things we're exposed to online. For instance, it determines Google results and the ads or social media posts you see. The list of how computer science uses algorithms is endless. So, we’ll mention some other real-life applications of algorithms and data structures.

**● **An **Array Data Structure** with a Sorting Algorithm helps sort contacts in your phone. **●** The

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Algorithms are a great way to develop logical thinking skills at educational levels. As a result, children and teenagers develop cross-disciplinary skills. These lead them to create original solutions. Moreover, algorithms are a big part of Machine Learning and Artificial Intelligence. Both of them are very prominent programming fields. Let's take a closer look.

Behind Artificial Intelligence (AI) lies a simple concept. Computers *can* learn based on existing decision-making examples. These algorithms can work with both structured and unstructured data. Machine Learning (ML) is a subfield of Artificial Intelligence, but, it can only work with structured data. This is what sets it apart from the main field of AI. AI and ML algorithms usage includes data mining and pattern recognition. To put it plainly, ML is what makes it possible for Netflix to give you recommendations. Also, it’s what allows Facebook to determine what to show on your news feed.

We have covered what algorithms are, as well as their uses. Also, we looked into its different types. What's more, now you have plenty of links and resources to help you learn more about this topic. We hope this article gives you an idea of how algorithms work and their essential role in our daily lives. Moreover, we hope we’ve made it easy to understand and made you curious enough to discover even more!

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