These Coding Algorithms Will Help You Crack Any Interview!
To land an exciting coding job, you must handle the interview questions like a pro! There is a plethora of coding algorithms from which the interviewer can pick any question to put your knowledge to the test. You must be well-versed in the types of algorithms that will come in handy while working with various programming languages.
However, it is natural to get cold feet before sitting for an interview as it is challenging to wrap your head around every concept of coding algorithms, especially when you are chasing the clock. If you need to revise all the important topics in one go, glance through this specially curated list of standard algorithms that can take you a step closer to your dream job.
But before we begin, it is important to understand what is algorithm in programming.
What are coding algorithms?
To solve a mathematical problem in a finite number of steps that often involve recursive simple operations is called an algorithm; in simple words, basic algorithms are a list of instructions. You come in contact with coding algorithms every day while using phones, laptops, or calculators. Algorithms help perform tasks in programming by feeding in tasks (input variables) to produce the desired outcome (output) and they can be presented in a variety of notation, including flowcharts, control tables, natural languages, etc.
Common coding algorithms to crack competitive interviews
These are some standard algorithms that interviewers often pick up during coding interviews:
1. Data structures and their applications
Data Structure is a way to store and organize data so that it can be used efficiently. There are a few ways to organize data with the help of structures. Array in C language is an example of data structure. An array is a collection of memory elements in which data is stored sequentially, i.e., one after another. In simple words, an array stores elements in a continuous manner. This organization of data is done with the help of an array of data structures. There are also other ways to organize the data in memory.
There are 7 data structures that every coder/developer should know about —
- Arrays
- Linked Lists
- Stacks
- Queues
- Trees
- Graphs
- Hash Tables/Sets
2. Recursion
The process in which a function calls itself directly or indirectly is called recursion and the corresponding function is called a recursive function. Recursion is a widely used phenomenon in computer science used to solve complex problems by breaking them down into simpler ones. Using recursive algorithms, certain problems can be solved quite easily.
3. Binary Search
Binary search works when provided with a sorted array of elements and a search key. Binary search functions by selecting the middle element and comparing it with the search key if the key is smaller than the left part of the middle element and traversed in the same way. The time complexity of a binary search is O(log n), where n is the number of elements in the array.
4. Dynamic Programming
Dynamic programming refers to the optimization of recessive functions by eliminating the need for recursive calling. Any recursive function in which a certain part of the code is called multiple times can be greatly improved with the implementation of dynamic programming. The recursiveness is removed by storing the results of the previous sub-function so that they do not have to be called back multiple times. This reduces the time complexity from exponential to polynomial time. Examples of algorithms that fall under the dynamic programming category are:
- Ugly Numbers
- Fibonacci Numbers
- Binomial Coefficient
- Permutation Coefficient
5. Merge Sort
Merge sort is based on the principles of divide and conquer algorithms. It refers to the practice of separating a problem into multiple parts, separately solving them, and finally merging them. Merge sort divides the array in half and calls the sort function on the two halves, those two halves are sorted and then merged using the merge function. The time complexity of merge sort is O(n log n).
6. Quick Sort
Quicksort works by selecting the last element as the pivot number and placing it in the middle with smaller numbers on the left while placing the larger ones on the right. Similar to merge sort, quick sort is also based on the divide and conquer algorithm, although it varies from merge sort in terms of its sorting functionality. The left and right sides are again called with the sort function which as a result sortes the whole array. The time complexity of quicksort is O(n^2).
7. Breadth-First Search
BFS is a list of instructions used for search that starts from the root. It searches in the neighborhood of the node on the same level. After a level is traversed, the efficient algorithm moves forward to the next level and keeps on traversing until the element is found. The time complexity of BFS is O(V + E).
8. Depth First Search
DFS is a search algorithm that starts the search process from the node and goes all the way down to the last leaf of the leftmost branch. After it has reached the leftmost leaf the algorithm starts backtracking and traverses the right side of the tree and so on. The issue with this DFS is that if a cycle exists, a certain node can be visited more than once. The time complexity of DFS is O(V + E), where V and E represent the number of vertices and edges respectively in the graph.
9. Custom Data structure
Sometimes the typical, pre-defined data structures do not get the job done and you need something better and more powerful. Custom data structures can be real or abstract objects depending on the uses of their data members. Data members can be considered as variables belonging to objects which are specified.
10. Sorting Algorithm
Sorting refers to arranging data in a particular format. The sorting algorithm specifies the way to arrange data in a particular order. Most common orders are in numerical or lexicographical order.
The importance of sorting lies in the fact that data searching can be optimized to a very high level if data is stored in a sorted manner. Sorting is also used to represent data in more readable formats.
11. Brute force algorithm
It is the simplest approach to a problem. A brute force algorithm is a primary approach to finding a solution when we see a problem. A brute force algorithm solves a problem through exhaustion: it goes through all possible choices until we get a solution. The time complexity of a brute force algorithm is often proportional to the input size. Brute force algorithms are simple and consistent, but very low in speed.
12. Backtracking Algorithm
The backtracking algorithm simply builds the solution by searching among all possible solutions. Using this algorithm, we continue building the solution following the criteria. Whenever a solution fails, we trace and paddle back to the failure point and build on the next solution and continue this process till a solution is found or all possible solutions are looked after.
13. Randomized Algorithm
In the randomized algorithm, we use a random number to get an immediate benefit. The random number helps in deciding the expected outcome.
14. Greedy Algorithm
In the Greedy algorithm, the solution is built in parts. The solution for the next part is built based on the immediate benefit of the next part. The one solution giving the most benefit will be chosen as the solution for the next part.
15. Hashing Algorithm
These efficient algorithms work similarly to the searching algorithm. But they contain an index with a key ID. In hashing, a key is assigned to specific data.
16. Linear time search
Linear time search is defined as a sequential search algorithm that starts at one end and goes through each element of a list until the desired element is found, otherwise the search continues till the end of the data set. It is the easiest searching algorithm.
17. Euclidean Algorithm
The euclidean algorithm is one of the efficient algorithms and methods for computing the greatest common divisor (GCD) of two integers (numbers), the largest number that divides them both without a remainder. It gets its name after the ancient Greek mathematician Euclid, who first described it in his Elements(c. 300 BC). It is a step-by-step procedure for performing a calculation according to well-defined rules. It is one of the oldest algorithms in common use. The euclidean algorithm can be used to reduce fractions to their simplest form and is a part of many other number-theoretic and cryptographic calculations.
Algorithm complexity and its types
A coding algorithm is defined as complex depending on the amount of space and time it consumes. There are two types of coding algorithm complexity:
Space complexity: The space complexity of a coding algorithm refers to the amount of memory required by the algorithm to store the variables and get the desired result. This can be for input variables, temporary operations, or outputs.
Time complexity: The time complexity of an algorithm refers to the amount of time required by the algorithm to execute and obtain results. This can be for normal operations, oop statements, conditional if-else statements, etc.
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