The ultimate guide to recursion interviews with strategies developed by FAANG engineers. Practice with real-world interview questions, detailed explanations, and visualizations to prep faster.

If you’ve got an interview coming up and want to brush up on your knowledge, or if you’ve ever struggled with solving coding problems using recursion, you'll find this course helpful.
 
You’ll start with the basics of what recursion is and why it’s important before diving into what it looks like in practice. You’ll see how recursion can help you solve a variety of different math, string, and data structure problems by using interactive code playgrounds that you can execute directly in your browser. You’ll have access to detailed explanations and visualizations for each problem to help you along the way.  
 
By the time you’re done, you’ll be able to use what you’ve learned to solve complex real-world problems and even advance more easily through your interviews at top tech companies.

Recursion for Coding Interviews in Java

## What is Topological Sort?

**Topological Sort** is a way to order a **directed acyclic** graph. A **directed** graph has edges that are  *incoming* or *outgoing*, meaning that they have a specific direction. An **acyclic** graph has *no cycles*, i.e., a node is not reachable from its ancestors. A topological sort takes a graph and finds the order of its nodes so it **always** starts from a node that has no incoming edges and then traverses the adjacent nodes. Note that the current node is before its adjacent. The illustration below will help explain this concept better. 

## Implementing the Code

The code below illustrates how to implement this process using recursion. First, let’s examine the code and then move on to its explanation.

Modify the edges by using the `addEdge` function and the number of vertices, `nVertices`, to create your own graph, `g`.Then, run the `topologicalSorting` on these variables to see how it works.

# What is Topological Sort?

**Topological Sort** is a way to order a **directed acyclic** graph. A **directed** graph has edges that are  *incoming* or *outgoing*, meaning that they have a specific direction. An **acyclic** graph has *no cycles*, i.e., a node is not reachable from its ancestors. A topological sort takes a graph and finds the order of its nodes so it **always** starts from a node that has no incoming edges and then traverses the adjacent nodes. Note that the current node is before its adjacent. The illustration below will help explain this concept better. 

# Implementing the Code

The code below illustrates how to implement this process using recursion. First, let’s examine the code and then move on to its explanation.

Modify the edges by using the `addEdge` function and the number of vertices, `nVertices`, to create your own graph, `g`.Then, run the `topologicalSorting` on these variables to see how it works.

This lesson will teach you how to write a recursive code for topological sorting in graphs.

Fundamentals of Recursion

Iteration to Recursion

Recursion with Numbers

Recursion with Strings

Recursion with Arrays

Recursion with Data Structures

Topological Sort in Graphs

What is Topological Sort?

Implementing the Code