08 Algorithms Every Developer Should Learn


08 Algorithms Every Developer Should Learn

Algorithms are commonly used in programming, and for a good reason. They provide a series of instructions for overcoming different software problems. Algorithms can make developers’ work more accessible by giving them access to various general problem-solving techniques.

Numerous programming techniques are available nowadays. Thus, proficient software engineers and developers must know which ones are available and when to utilize them. An efficient algorithm will choose the approach that completes a task or solves a problem in the quickest and least memory-intensive manner.

Many problems can be resolved by starting with some of the most popular algorithms, depending on the function required.

1. Binary Search

Binary search is one of the first things taught in any computer science class. It is the simplest example of how a little ingenuity can make things exponentially more efficient.

A binary search consists of taking a sorted array, iteratively splitting the collection into two, and comparing an element you are looking for against each half until you find the feature.

2. Quicksort and Mergesort

Sorting algorithms are great for getting comfortable with arrays, but Quicksort and Mergesort are efficient enough to be used in severe applications. Being comfortable implementing these sorting algorithms, these algorithms are essential to being a serious developer.

3. Huffman Coding

Huffman coding is the foundation of modern text compression. It works by considering how often different characters appear in a text and organizing them in a tree based on this frequency.


4. Breadth-First Search

Again, trees are at the heart of many algorithms, and software developers work with them. Understanding basic tree traversal is a top priority for an aspiring developer. 

Breadth-first search explores a tree level by level until the target node is found. Since it goes through every level, it is guaranteed to find a solution. 


5. Depth First Search

Continuing with tree traversal, Depth-First Search is the other primary approach for finding an element in a tree. Instead of working down the tree level by level, it explores the tree branch by branch.

6. Gradient Descent

For many developers, Gradient Descent is not necessarily going to be helpful. Gradient Descent will be at the heart of your work if you touch anything with regression or machine learning. Gradient Descent is a method of procedure optimizing functions using calculus.


In the context of regression and machine learning, this means finding specific values that minimize the error in your prediction algorithm. While it is undoubtedly more mathematically involved than a lot of these other algorithms, if you are working significantly with data and predictions, understanding how gradient descent works are significant.

7. Dijkstra’s Algorithm

Another fundamental issue that developers work with is pathfinding. Graphs are an extremely versatile way to describe problems involving networks of distinct objects.

Dijkstra’s algorithm is a way of finding the quickest path between two nodes in a graph. It is the foundation of most work done in pathfinding and finds itself used in anything from artificial intelligence to game design.

06 Algorithms

8. Diffie-Hellman Key Exchange

The Diffie-Hellman Key Exchange is an excellent introduction to how cryptography works. More specifically, a Diffie-Hellman Key Exchange combines public and private keys(Effectively long numbers) to encrypt information when it is transferred between different parties.

Even if you’re not working in cybersecurity, understanding encryption and secure communication are fundamental to working as a developer. Additionally, even though Diffie-Helman is far from the best algorithm, it is effortless to implement and is similar enough to most other encrypted communication methods.

Summing Up 

Only memorizing the algorithms, you never make you an expert developer. Understanding challenges and creating solutions are at the core of software engineering. Learning algorithms is unnecessary because you must carefully implement them for whatever you are creating. They are essential because they impart problem-solving skills.

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