Algorithms can make or break software. Take your algorithms from good to great and ace your coding interview today. 

Algorithmic paradigms, asymptotic complexity, and more.

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Algorithms are the basis for any great program. Tech companies work to develop the best, most efficient algorithms to reduce computational stress and improve load times. Generally, they save time and money all while creating a product that is fast and easy to use. Needless to say, if you want to advance your career, mastering algorithms is a must. Technical interviews are notorious for asking questions about algorithms.

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Algorithms play such an important role in your interview process that your answers to certain questions may filter you to different levels of seniority. If you apply for a senior developer position and end up giving an unsatisfactory answer to a technical question, the hiring team may downlevel your application to a mid-level developer role. If you're prepared, however, just the opposite may happen. An interviewer may be so impressed with your responses that they consider you for a higher position. 

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If you have an interview coming up or you're just dusting off some skills in preparation, you may have found that there's a lot more to cover than you remembered. And, you may have noted that the amount of material out there aiming to cover this topic is a bit daunting. 

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Technical interviews are often the most stressful part of the interview process. You must prepare effectively to perform to the best of your ability. If you're feeling overwhelmed, keep these steps in mind instead of trying to face the bombardment of information head-on.

1. Know the basics.

2. Understand the use cases for the common algorithmic paradigms.

3. Be prepared to optimize your program with asymptotic analysis.

If you refer to these goals, you can fight information overload by starting to break down and compartmentalize what you learn. On this page, we'll give you an overview for the majority of topics that you should be familiar with before a technical interview. We won't get too detailed on any one area, but you should form an idea of where you'll need to drill deeper based on your individual knowledge.

The basics of algorithms

Algorithms are essentially a set of deliberate instructions that aim to solve one particular problem. Learning to identify the type of algorithm your problem calls for will help you lay the mental groundwork for fully understanding them in the long run. 

You can start by familiarizing yourself with three of the most common types:

• Sorting/searching: Examples of sorting algorithms are: insertion sort, bubble sort, selection sort, mergesort, quicksort. Search algorithms are linear search and binary search.

• Graph: Graph algorithms like breadth-first search (BFS) and depth-first search (DFS) are used to traverse graphs. Graphs represent objects as nodes (vertices), and the relationships between those objects as edges (lines), often forming a network.

• Shortest path: Algorithms like Djikstra's and Bellman-Ford's are used to find the shortest possible path in a graph.

You can start preparing by studying popular algorithms that every dev should know. One of the first things to refresh is recursion. Recursion algorithms are often a core problem solving approach for some of the most common interview problems.

algorithmic paradigms

An algorithmic paradigm is essentially a design class of algorithms. Paradigms inform what the algorithm is capable of as well as a general structure for how it works.

• Brute force: One of the simplest but least efficient algorithms, brute force involves checking every single possible outcome. 

• Divide and conquer: Recursively solves problems by segmenting a larger task into smaller components and reassembles them upon completion.

• Dynamic programming: Similar to divide and conquer except the subtasks overlap. Completed overlapping sections can be retrieved from memory.

• Greedy: Solves subtasks using the best possible solution available, or the local optima. 

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Other algorithmic frameworks are: approximation, randomized, and linear.

Asymptotic complexity

Asymptotic time complexity refers to the analysis of the exact run time of an algorithm. The calculation is independent of the platform or inputs. Asymptotic space complexity refers to the amount of memory an algorithm consumes. Both complexities are a measure of the efficiency of an algorithm and something all software developers need to be aware of.

There are three asymptotes that need to be calculated to develop an appropriate picture of an algorithm.

• Big Omega (Ω): Represents the best case.

• Big Theta (Θ): Represents the average case.

• Big O Notation (O): Represents the worst case.

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Typically, Big O notation is the most common metric used for measuring efficiency. When benchmarking programs, the worst case scenario is what will make or break the functionality. You may well be asked to calculate the Big O complexity of an algorithm in an interview. 

A couple of general best practices for Big O notation are:

• Ignore the lower order terms

• Drop the leading constants

For a more in-depth glossary of concepts you should be familiar with, refer to this guide to computer science fundamentals for developers.

Start preparing for algorithm interview questions today

If you're ready to start your algorithm interview prep, don't waste time wading through the glut of information a web search turns up, trying to cobble together bits of information from blogs and YouTube tutorials. You're better suited using a comprehensive approach. The Educative platform is more interactive than passive learning through videos. You engage with the material through in-browser coding environments and address gaps in your knowledge to help actively home in on what you need to learn. The Algorithms and Data Structures Interview Crash Course will do more than just prepare you for an interview. It will allow you to retain what you learn so you can further your career even after you get the job.