This course helps you crack the coding interview by mastering the solution of real coding interview problems using various algorithms in Java.

Intermediate

151 Lessons

15h

Certificate of Completion

This course helps you crack the coding interview by mastering the solution of real coding interview problems using various algorithms in Java.

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Explanations

This course includes

140 Playgrounds

50 Challenges

17 Quizzes

This course includes

140 Playgrounds

50 Challenges

17 Quizzes

Course Overview

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'l...

TAKEAWAY SKILLS

Java

Programming Language

Prepare For Interview

Algorithms

Time Complexity

Course Content

1.

Introduction

In this chapter, you will discover how to enhance your coding skills in Java through algorithmic concepts and practical challenges for technical interviews.

2.

Algorithmic Paradigms

This chapter covers algorithms, like brute force, greedy approach, divide and conquer, and dynamic programming, with their strategies and applications.

3.

Asymptotic Analysis

In this chapter, you will learn how to evaluate algorithms' efficiency through asymptotic analysis, focusing on time complexity and practical examples.

Comparing AlgorithmsExample: Measuring Time Complexity of a Single Loop AlgorithmExample: Time Complexity of an Algorithm With Nested LoopsIntroduction to Asymptotic Analysis and Big (O)Other Common Asymptotic Notations and Why Big (O) Trumps ThemUseful FormulaeCommon Complexity ScenariosChallenge: Big (O) of Nested Loop with AdditionSolution: Big (O) of a Nested Loop with AdditionChallenge: Big (O) of Nested Loop with SubtractionSolution: Big (O) of a Nested Loop with SubtractionChallenge: Big (O) of Nested Loop with MultiplicationSolution: Big (O) of Nested Loop with MultiplicationChallenge: 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

4.

Sorting and Searching

This chapter explores sorting and searching algorithms, detailing their efficiencies and challenges to improve your problem-solving skills in coding interviews.

Selection Sort, Bubble Sort, and Insertion SortMerge SortQuicksortTime Complexities of Sorting Algorithms (Overview)Searching AlgorithmsChallenge: Find Two Numbers that Add up to "n"Solution: Find Two Numbers that Add up to "n"Challenge: Search in a Rotated ArraySolution: Search in a Rotated ArrayChallenge: Group AnagramsSolution: Group AnagramsChallenge: Find the Median of Two Sorted ArraysSolution: Find the Median of Two Sorted ArraysChallenge: Find Duplicates in ArraySolution: Find Duplicates in ArrayChallenge: Search in Sorted MatrixSolution: Search in Sorted MatrixChallenge: Count Element OccurrenceSolution: Count Element OccurrenceChallenge: Search Insert PositionSolution: Search Insert PositionChallenge: Sparse SearchSolution: Sparse SearchChallenge: Dutch National Flag ProblemSolution: Dutch National Flag ProblemQuiz on Sorting & Searching

5.

Dynamic Programming

This chapter focuses on dynamic programming techniques, emphasizing optimization for algorithmic challenges to enhance efficiency in problem-solving.

Fibonacci Series Using RecursionIntroducing Dynamic Programming With Fibonacci NumbersMemoizing Fibonacci NumbersTabulating Fibonacci NumbersChallenge: The 0/1 Knapsack ProblemSolution: The 0/1 Knapsack ProblemChallenge: Staircase ProblemSolution: Staircase ProblemChallenge: The Partition ProblemSolution: The Partition ProblemChallenge: Longest Common SubstringSolution: Longest Common SubstringChallenge: Shortest Common SupersequenceSolution: Shortest Common SupersequenceChallenge: Longest Palindromic SubsequenceSolution: Longest Palindromic SubsequenceChallenge: The Coin Change ProblemSolution: Coin Change ProblemChallenge: Egg Dropping ProblemSolution: Egg Dropping ProblemChallenge: Strings InterleavingSolution: Strings InterleavingChallenge: Edit Distance ProblemSolution: The Edit Distance ProblemQuiz on Dynamic Programming

6.

Greedy Algorithms

20 Lessons

In this chapter, you will learn how to solve problems using greedy algorithms by making optimal local choices for efficient solutions.

7.

Divide and Conquer

23 Lessons

This chapter covers the divide and conquer approach, illustrating its applications, challenges, and efficient solutions for various problems.

8.

Graph Algorithms

22 Lessons

In this chapter, you will explore essential graph algorithms, including various traversals such as BFS and DFS, and their practical implementations.

9.

Appendix: Auxiliary Source Code

5 Lessons

This chapter discusses essential Java data structures like Stacks, Queues, PriorityQueues, HashMaps, and ArrayLists.

10.

Conclusion

2 Lessons

This chapter shows how to solve technical interview preparation by emphasizing algorithm analysis and suggesting further specialized courses.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

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Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

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Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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