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Grokking the Coding Interview Patterns in C#

The ultimate guide to coding interviews in C#, developed by FAANG engineers. Master essential patterns, tackle tough questions, and prep faster for interviews at top tech companies.

4.9
1125 lessons
17 AI mock interviews
85h
Created by Fahim ul Haq
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Grokking the Coding Interview Patterns in C#
What's included
  • 1125 lessons across 31 modules
  • 578 quizzes with instant feedback
  • 17 AI mock interviews with scoring rubrics
  • AI Tutor on every lesson
Why this course
  • Ace Coding InterviewsDemonstrate proficiency in solving complex coding problems using established patterns and techniques, ready for technical interviews.
  • Optimize Algorithm PerformanceImplement efficient algorithms that reduce time and space complexity, ensuring quick solutions to coding challenges.
  • Master Data StructuresDesign and utilize custom data structures that enhance data management and retrieval, applicable in real-world software development.
  • Solve Mathematical ProblemsApply mathematical reasoning and geometric concepts to solve coding challenges involving numbers, shapes, and coordinates.
Who wrote it
Fahim ul Haq
Software Engineer, Distributed Storage at Meta and Microsoft, Educative (Co-founder & CEO)

Fahim ul Haq is the co-founder and CEO of Educative, a hands-on learning platform for software engineers. He spent nearly a decade as a software engineer at Microsoft and Meta, where he gained deep experience building large-scale distributed systems. His work spanned Project Red Dog at Microsoft, which evolved into Microsoft Azure, and Scuba, Meta's real-time data analytics engine. In 2015, Fahim co-founded Educative to help developers get hands-on with new skills. Fahim's courses have helped hundreds of thousands of engineers learn System Design and prepare for technical interviews. Fahim earned his M.S. in Computer Science from the University of Washington in 2015.

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What You'll Learn

  • Analyze and implement coding interview patterns using data structures and algorithms to solve complex problems effectively.
  • Apply depth-first search and breadth-first search techniques to traverse and manipulate trees and graphs in various scenarios.
  • Utilize dynamic programming strategies to optimize solutions for problems involving sequences, paths, and combinations.
  • Implement efficient algorithms for string manipulation, including encoding, decoding, and pattern matching, to enhance problem-solving skills.
  • Design and optimize custom data structures to manage and retrieve data efficiently in coding interviews.
  • Develop mathematical reasoning and geometric problem-solving skills to tackle challenges involving coordinates, distances, and shapes.

Learning Roadmap

1.

Two Pointers

The Two Pointers pattern uses two coordinated pointers or iterators to traverse data efficiently.

Introduction to Two Pointers
Valid Palindrome
easy
3Sum
med
Remove nth Node from End of List
med
Sort Colors
med
Reverse Words in a String
med
Valid Word Abbreviation
easy
Valid Palindrome II
easy
Lowest Common Ancestor of a Binary Tree III
med
Strobogrammatic Number
easy
Minimum Number of Moves to Make Palindrome
hard
Next Palindrome Using Same Digits
hard
Count Subarrays With Fixed Bounds
hard
Find the Lexicographically Largest String From Box II
hard
Get the Maximum Score
hard
Create Maximum Number
hard
Append Characters to String to Make Subsequence
med
Squares of a Sorted Array
easy
Reverse String
easy
Intersection of Two Linked Lists
easy
Partition Labels
med
Remove Element
easy
String Compression
med
Rotate Array
med
Next Permutation
med
Remove Duplicates from Sorted Array
easy
Reverse Vowels of a String
easy
Is Subsequence
easy
Merge Strings Alternately
easy
Compare Version Numbers
med
Move Zeroes
med
Longest Subarray of 1's After Deleting One Element
med
Backspace String Compare
med
Next Greater Element III
med
Rotating the Box
med
Count the Number of Fair Pairs
med
Remove Duplicates from Sorted List II
med
Sum of Square Numbers
med
Shuffle the Array
med
Rotate String
med
Max Number of K-Sum Pairs
med
Repeated Substring Pattern
med
Reverse Words in a String III
med
3Sum Closest
med
Push Dominoes
med
Rearrange Array Elements by Sign
med
Reverse String II
med
Sort Array By Parity
med
Long Pressed Name
med
Valid Mountain Array
med
Remove Duplicates from Sorted Array II
med
Two Sum II - Input Array Is Sorted
med
Swap Adjacent in LR String
med
Expressive Words
med
Last Substring in Lexicographical Order
med
Bag of Tokens
med
Check If String Is a Prefix of Array
med
Reformat Phone Number
med
K-diff Pairs in an Array
med
Longest Mountain in Array
med
Meeting Scheduler
med
Maximize Greatness of an Array
med
Find the Original Typed String I
med
Find Beautiful Indices in the Given Array II
med
Zigzag Iterator
med
Length of Last Word
med
Capitalize the Title
med
Number of Distinct Averages
med
Apply Operations to an Array
med
Maximum Enemy Forts That Can Be Captured
med
Minimum Common Value
med
Merge Two 2D Arrays by Summing Values
med
Remove Trailing Zeros From a String
med
Lexicographically Smallest Palindrome
med
Check if a String Is an Acronym of Words
med
Substring Matching Pattern
med
Generate Tag for Video Caption
med
Reverse String Prefix
med
The k Strongest Values in an Array
med
Ways to Split Array Into Three Subarrays
med
Minimize Maximum Pair Sum in Array
med
Adding Spaces to a String
med
Watering Plants II
med
Partition Array According to Given Pivot
med
Move Pieces to Obtain a String
med
Check if All A's Appears Before All B's
med
Find the Encrypted String
med
Trim Trailing Vowels
med
Number of Subsequences That Satisfy the Sum Condition
med
One Edit Distance
med
3Sum Smaller
med
Sort Transformed Array
med
Reverse Only Letters
med
3Sum With Multiplicity
med
Intersection of Three Sorted Arrays
med
Shortest Subarray to be Removed to Make Array Sorted
med
Split Two Strings to Make Palindrome
med
Check If Two String Arrays are Equivalent
med
Form Array by Concatenating Subarrays of Another Array
med
Minimum Length of String After Deleting Similar Ends
med
Maximum Score of a Good Subarray
med
Sentence Similarity III
med
Maximum Distance Between a Pair of Values
med
Longest Common Subsequence Between Sorted Arrays
med
Two Furthest Houses With Different Colors
med
Valid Palindrome IV
med
Maximum Matching of Players With Trainers
med
Merge Operations to Turn Array Into a Palindrome
med
Subsequence With the Minimum Score
med
Find the Maximum Number of Marked Indices
med
Sorting Three Groups
med
Minimum Array Length After Pair Removals
med
The Wording Game
med
Count the Number of Incremovable Subarrays I
med
Find Beautiful Indices in the Given Array I
med
Minimize Length of Array Using Operations
med
Minimum Time to Revert Word to Initial State I
med
Rotate Non Negative Elements
med
Concatenate Array With Reverse
med
Compare Sums of Bitonic Parts
med
3.

Sliding Window

Uses a window, either fixed or dynamic, to optimize the analysis of sequential data (substring or subarray) through iterative processing, minimizing the need for nested loops.

4.

Intervals

The Intervals pattern is a powerful algorithmic technique for solving problems involving ranges of values, such as time spans or numeric ranges.

5.

In-Place Manipulation of a Linked List

In-place manipulation of a linked list refers to modifying the linked list directly by changing the pointers between existing nodes, without creating a new linked list or using extra data structures.

6.

Two Heaps

A heap is a specific data structure with a fixed ordering (min or max), while a priority queue is an abstract data type that handles custom priority requirements for elements.

7.

K-way Merge

Learn how to use k-way merge for efficient data merging and finding k-th small elements.

8.

Top K Elements

The Top K Elements pattern focuses on efficiently identifying the k largest, smallest, or most frequent elements in a dataset.

9.

Modified Binary Search

It applies a binary search technique with variations to efficiently solve problems like finding specific targets, peaks (first/last occurrence), or rotated array elements by reducing the search space.

10.

Subsets

The Subsets pattern is an algorithmic strategy used to generate all possible combinations or subsets of a given set of elements.

11.

Greedy Techniques

Makes locally optimal choices at each step, aiming for a globally optimal solution in problem-solving scenarios, often without reassessment.

12.

Backtracking

Systematically explores all possibilities to find solutions, often used in problems with multiple decision points, exploring, and undoing choices.

13.

Dynamic Programming

Solves complex problems by breaking them into smaller overlapping subproblems and storing results to avoid redundant computations, optimizing time and space.

14.

Cyclic Sort

Sorts an array of integers from 1 to n in place by repeatedly swapping the elements until each is placed in its correct position.

15.

Topological Sort

Determines a valid linear ordering of nodes in a directed acyclic graph that has dependencies on each other, crucial in tasks like scheduling or dependency resolution.

16.

Sort and Search

Combines sorting and searching techniques to effectively process and retrieve data, often using sorting algorithms and binary search to optimize problem-solving in datasets that can be ordered.

17.

Matrices

Combines sorting and searching techniques to effectively process and retrieve data, often using sorting algorithms and binary search to optimize problem-solving in datasets that can be ordered.

18.

Stacks

Operates on a last-in, first-out principle, where elements are added and removed from the same end, facilitating ordered data processing and retrieval.

19.

Graphs

TBA

20.

Tree Depth-First Search

Traverses a tree by exploring as deeply as possible along each branch before backtracking, which is useful for tasks like traversal, pathfinding, or search operations.

21.

Tree Breadth-First Search

Explores nodes of a tree level by level, ensuring each level is processed before moving to the next, often using a queue to manage traversal.

22.

Trie

Organizes data in a tree structure where each node represents a character, enabling efficient insertion, deletion, and search operations for words.

23.

Hash Maps

Utilizes a data structure that maps keys to values, enabling efficient insertion, deletion, and retrieval operations for associative arrays.

24.

Knowing What to Track

The Knowing What to Track pattern focuses on identifying and maintaining only the key variables or states necessary to solve a problem efficiently, such as counts, indices, or states.

25.

Union Find

Maintains disjoint sets, supporting operations to determine the set to which an element belongs (find) and merge two sets (union).

26.

Custom Data Structures

27.

Bitwise Manipulation

It uses binary operations to efficiently perform calculations, comparisons, or transformations directly on bits, optimizing for low-level data control.

28.

Math and Geometry

Applies mathematical concepts and geometric properties to solve problems efficiently, often involving calculations, number properties, coordinate geometry, or transformations.

29.

Challenge Yourself

Test your understanding of various coding challenges focusing on algorithm efficiency.

Course Author:

About this course
WHY I WROTE THIS COURSE
Preparing for a coding interview often turns into a grind, with hundreds of problems, scattered topics, and no clear signal of progress. But if you look at how interviews are actually designed at companies like Google, Meta, and Amazon, a different picture emerges. These interviews consistently rely on a small set of coding interview patterns. The challenge isn’t volume. It’s recognizing those patterns and applying them under pressure. I built this course after working closely with how candidates prepare and where they get stuck. The recurring issue wasn’t effort, but the approach. Many candidates could solve problems they had seen before, but struggled when a question was framed differently. What they needed was a structured way to think. That’s the foundation behind Grokking the Coding Interview Patterns. The course is organized around these core patterns rather than isolated questions. You’ll learn how to identify patterns like sliding window, two pointers, and graph traversal, understand the intuition behind them, and apply them across different problem types. Each concept is reinforced through hands-on practice, with implementations available in Python, C++, Java, JavaScript, and Go, so you can prepare in the language you’ll use in your interview. This pattern-first approach has helped a large number of developers prepare more efficiently and perform better across interview rounds. If you want to move beyond memorization and build a reliable way to solve problems, this course gives you a clear path to mastering the coding interview.
ABOUT THE AUTHOR

Fahim ul Haq

Software Engineer, Distributed Storage at Meta and Microsoft, Educative (Co-founder & CEO)

Learn more about Fahim

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Frequently Asked Questions

Is C# good for coding interviews?

Yes, C# is suitable for coding interviews. It offers extensive features, including various data structures, strong typing, and powerful libraries, which are useful for solving various coding problems. Its object-oriented design and extensive .NET framework support make it a good choice for demonstrating problem-solving skills and writing efficient, maintainable code in technical interviews. Its similarity with Java makes it even more popular.

How to prepare for a C# Coding interview?

To prepare for a C# coding interview, focus on mastering core concepts such as data structures (arrays, lists, dictionaries), algorithms (sorting, searching), and object-oriented programming (classes, inheritance). Practice coding problems on platforms like Educative, LeetCode, and HackerRank to apply these concepts and become familiar with specific features in C# like LINQ and async/await.

Can I use C# in the Google interview?

Yes, you can use C# in a Google interview. Google allows a range of programming languages for coding challenges, and C# is a viable option, especially if you are comfortable with its syntax and features. Make sure to demonstrate your problem-solving skills and coding proficiency properly. Study and practice Grokking Coding Interview Patterns in the C# course by Educative.

How to crack coding interviews easily?

To crack coding interviews easily, focus on mastering the basics, practicing regularly, learning common patterns, doing mock interviews, reviewing and reflecting, and communicating clearly. Educative offers Grokking Coding Interview Patterns in six languages and AI mock interviewers to help you crack the coding interviews easily.

Can I practice coding in grokking the coding interview C# course?

Yes, you can practice coding in Grokking the Coding Interview with C#. Educative offers 228 coding problems categorized by patterns to help you prepare thoroughly.

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