HomeCoursesAlgorithms for Coding Interviews in Java

Intermediate

15h

Algorithms for Coding Interviews in Java

The ultimate guide to coding interviews in Java. Developed by FAANG engineers, tackle 50+ challenge problems, review detailed solutions, and learn strategies to craft optimal solutions.

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Overview

Content

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Algorithms are one of the most common themes in coding interviews, so having a firm grip on them can be the difference between being hired or not. After this comprehensive course in Java, one of the most popular coding languages, you'll have an in-depth understanding of different algorithm types and be equipped with a simple process for approaching complex analysis. As you progress through the course, you’ll be exposed to the most important algorithms that you're likely to encounter in an interview. You'll work your way through over 50 interactive coding challenges and review detailed solutions for each problem. And guess what? Even if you’re not yet fully comfortable with Java, you’ll walk away with the ability to craft optimal solutions for addressing tough coding interview questions in it.

Algorithms are one of the most common themes in coding interviews, so having a firm grip on them can be the difference between b...Show More

TAKEAWAY SKILLS

Java

Programming Language

Prepare for Interview

Algorithms

Time Complexity

Content

151 Lessons17 Quizzes50 Challenges

Introduction

2 Lessons

Get familiar with foundational concepts, coding challenges, and advanced problem-solving strategies for Java.

Intended Audience

Learning Outcomes

Algorithmic Paradigms

4 Lessons

Grasp the fundamentals of brute force, greedy algorithms, divide and conquer, and dynamic programming.

Asymptotic Analysis

22 Lessons

Master the steps to asymptotic analysis, comparing algorithm complexities, and solving complexity challenges.

Comparing Algorithms

Example: Measuring Time Complexity of a Single Loop Algorithm

Example: Time Complexity of an Algorithm With Nested Loops

Introduction to Asymptotic Analysis and Big (O)

Other Common Asymptotic Notations and Why Big (O) Trumps Them

Useful Formulae

Common Complexity Scenarios

Challenge: Big (O) of Nested Loop with Addition

Solution: Big (O) of a Nested Loop with Addition

Challenge: Big (O) of Nested Loop with Subtraction

Solution: Big (O) of a Nested Loop with Subtraction

Challenge: Big (O) of Nested Loop with Multiplication

Solution: Big (O) of Nested Loop with Multiplication

Challenge: Nested Loop with Multiplication (Basic)

Solution: Nested Loop with Multiplication (Basic)

Challenge: Nested Loop with Multiplication (Intermediate)

Solution: Nested Loop with Multiplication (Intermediate)

Challenge: Nested Loop with Multiplication (Advanced)

Solution: Nested Loop with Multiplication (Advanced)

Challenge: Nested Loop with Multiplication (Pro)

Solution: Nested Loop with Multiplication (Pro)

Complexity Interview Questions

Sorting and Searching

26 Lessons

Discover algorithm techniques for sorting (like Merge Sort and QuickSort) and efficient searching methods.

Selection Sort, Bubble Sort, and Insertion Sort

Merge Sort

Quicksort

Time Complexities of Sorting Algorithms (Overview)

Searching Algorithms

Challenge: Find Two Numbers that Add up to "n"

Solution: Find Two Numbers that Add up to "n"

Challenge: Search in a Rotated Array

Solution: Search in a Rotated Array

Challenge: Group Anagrams

Solution: Group Anagrams

Challenge: Find the Median of Two Sorted Arrays

Solution: Find the Median of Two Sorted Arrays

Challenge: Find Duplicates in Array

Solution: Find Duplicates in Array

Challenge: Search in Sorted Matrix

Solution: Search in Sorted Matrix

Challenge: Count Element Occurrence

Solution: Count Element Occurrence

Challenge: Search Insert Position

Solution: Search Insert Position

Challenge: Sparse Search

Solution: Sparse Search

Challenge: Dutch National Flag Problem

Solution: Dutch National Flag Problem

Quiz on Sorting & Searching

Dynamic Programming

25 Lessons

Deepen your knowledge of dynamic programming techniques for solving complex coding problems efficiently.

Fibonacci Series Using Recursion

Introducing Dynamic Programming With Fibonacci Numbers

Memoizing Fibonacci Numbers

Tabulating Fibonacci Numbers

Challenge: The 0/1 Knapsack Problem

Solution: The 0/1 Knapsack Problem

Challenge: Staircase Problem

Solution: Staircase Problem

Challenge: The Partition Problem

Solution: The Partition Problem

Challenge: Longest Common Substring

Solution: Longest Common Substring

Challenge: Shortest Common Supersequence

Solution: Shortest Common Supersequence

Challenge: Longest Palindromic Subsequence

Solution: Longest Palindromic Subsequence

Challenge: The Coin Change Problem

Solution: Coin Change Problem

Challenge: Egg Dropping Problem

Solution: Egg Dropping Problem

Challenge: Strings Interleaving

Solution: Strings Interleaving

Challenge: Edit Distance Problem

Solution: The Edit Distance Problem

Quiz on Dynamic Programming

Greedy Algorithms

20 Lessons

Follow the process of designing and implementing efficient greedy algorithms to solve diverse optimization problems.

What is a Greedy Approach?

Challenge: Make a Change Machine

Solution: Make Your own Change Machine

Challenge: Connecting 'n' Pipes with Minimum Cost

Solution: Connecting 'n' Pipes with Minimum Cost

Challenge: Find the Egyptian Fraction

Solution: Find the Egyptian Fraction

Challenge: Minimum Number of Platforms Required for Train Station

Solution: Minimum Number of Platforms Required for TrainStation

Challenge: Help the Police Officers Catch the Thieves

Solution: Help the Police Officers Catch the Thieves

Challenge: Fractional Knapsack

Solution: Fractional Knapsack Problem

Challenge: Find the Largest Number

Solution: Find the Largest Number

Challenge: Color the Graph

Solution: Color the Graph

Challenge: Find the Minimum Spanning Tree

Solution: Kruskal’s Minimum Spanning Tree

Quick Quiz!

Divide and Conquer

23 Lessons

Master the steps to applying divide and conquer for efficient problem-solving in algorithm design.

Introduction to Divide and Conquer with Binary Search

Pascal's Triangle

Challenge: Euclidean Algorithm

Solution Review 1: Euclidean Algorithm

Challenge: Peak Element

Solution: Peak Element

Challenge: Maximum Subarray Sum

Solution: Maximum Subarray Sum

Challenge: Missing Number in Sorted Array

Solution: Missing Number in Sorted Array

Challenge: Find the Closest Number

Solution: Find the Closest Number

Challenge: Shuffle Integers

Solution: Shuffle Integers

Challenge: Collect Coins in Minimum Steps

Solution: Collect Coins in Minimum Steps

Challenge: Merge a Number of Sorted Arrays

Solution: Merge a Number of Sorted Arrays

Challenge: Find the Floor and Ceiling of a Number

Solution: Find the Floor and Ceil of a Number

Challenge: Inversion Count in an Array

Solution: Inversion Count in an Array

Quiz on Divide and Conquer

Graph Algorithms

22 Lessons

Sharpen your skills in implementing and solving diverse graph algorithms efficiently.

Introduction to Graph Algorithms and Implementation

Challenge: Implement Breadth First Search

Solution: Implement Breadth First Search

Challenge: Implement Depth-First Search

Solution: Implement Depth-First Search

Challenge: Calculate the Number of Nodes in a Given Graph Level

Solution: Number of Nodes in a Given Graph Level

Challenge: Print the Transpose of a Graph

Solution: Print the Transpose of a Graph

Challenge: Count the Paths Between Two Nodes

Solution: Count the Paths Between Two Nodes

Challenge: Check if a Graph is Strongly Connected

Solution: Check if a Graph is Strongly Connected

Challenge: Print all the Connected Components in a Graph

Solution: Print all the Connected Components of a Graph

Challenge: Removing a Given Edge

Solution: Removing an Edge

Challenge: Check If Given Graph is Bipartite

Solution: Check if a Given Graph is Bipartite

Challenge: Topological Sorting of a Graph

Solution: Topological Sorting of a Graph

Quiz on Graph Algorithms

Appendix: Auxiliary Source Code

5 Lessons

Unpack the core of auxiliary classes like Stack, Queue, PriorityQueue, HashMap, and ArrayList.

10.

Conclusion

2 Lessons

Master the steps to understand efficient algorithms and enhance your technical interview skills.

Overview

Where to go from here

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Coderust

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AI Prompt

Build prompt engineering skills. Practice implementing AI-informed solutions.

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Evaluate and debug your code with the click of a button. Get real-time feedback on test cases, including time and space complexity of your solutions.

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Free Resources

Frequently Asked Questions

How do I prepare for a Java coding interview?

Focus on mastering data structures (arrays, linked lists, stacks, queues, trees, graphs) and algorithms (sorting, searching, dynamic programming). Practice on platforms like Educative. Get comfortable with Java-specific features like collections, generics, and streams. Time management is crucial, so simulate timed coding interviews.

Is Java a good choice for coding interviews?

Yes, Java is a great language for coding interviews. It offers a balance of readability, ease of debugging, and rich built-in libraries for data structures and algorithms. Many top tech companies expect candidates to use languages like Java for coding interviews.

How do I improve my Java coding skills?

Regularly practice coding problems, explore open-source projects, and review Java’s core concepts like OOP, exception handling, and multithreading. Read code written by experienced developers, contribute to Java forums, and stay updated with Java’s latest features.

How do I write fast code in Java?

Write efficient algorithms by understanding time and space complexity. Use appropriate data structures from Java’s collections framework and avoid unnecessary memory allocations. Opt for primitive data types where possible and utilize Java’s profiling tools to identify and optimize bottlenecks.

Why is Java good for algorithms?

Java is widely used for algorithms due to its high-level abstraction and performance balance. Its rich libraries (like java.util) provide efficient implementations of common data structures, making it easy to focus on problem-solving. Additionally, Java’s strict typing helps catch errors early during development.