LeetCode vs. Educative-99
Practice makes perfect, and drilling problem sets is excellent for preparing for unknown territory in the coding interview. By helping facilitate this tried-and-true practice, interview prep platforms like LeetCode have boomed in popularity.
But LeetCode alone won't prepare you for the unknown.
Having interviewed at Microsoft and Meta (and now, Educative), I can tell when a developer has relied on LeetCode: they often struggle with unfamiliar problems.
To help you get ahead, I'll be sharing why relying on LeetCode alone can be your pitfall, and what LeetCode alternatives could better prepare you and foolproof your coding interview prep.
Grokking the Coding Interview Patterns
With thousands of potential questions to account for, preparing for the coding interview can feel like an impossible challenge. Yet with a strategic approach, coding interview prep doesn’t have to take more than a few weeks. Stop drilling endless sets of practice problems, and prepare more efficiently by learning coding interview patterns. This course teaches you the underlying patterns behind common coding interview questions. By learning these essential patterns, you will be able to unpack and answer any problem the right way — just by assessing the problem statement. This approach was created by FAANG hiring managers to help you prepare for the typical rounds of interviews at major tech companies like Apple, Google, Meta, Microsoft, and Amazon. Before long, you will have the skills you need to unlock even the most challenging questions, grok the coding interview, and level up your career with confidence. This course is also available in JavaScript, Python, Go, and C++ — with more coming soon!
Preparing for a coding interview#
Coding interviews are essential for assessing your problem-solving skills, and understanding of computer science fundamentals, data structures, and algorithms. You may also be asked to analyze the time and space complexity of solutions, debug and test code, and tackle real-world scenarios.
There's a lot to do—so it makes sense that practice is key to success. It's not enough to study concepts, you have to see and put them in action yourself. One of the most common approaches to getting hands-on practice is through platforms like LeetCode (or LeetCode alternatives like HackerEarth, HackerRank, etc).
The LeetCode way#
Platforms like LeetCode are a great way to tackle coding problems of various difficulty levels. With various languages and a big developer community, LeetCode and its kind are certainly great resources.
But these platforms alone can't prepare you for the unknown. They have major limitations.
Their biggest limitations?
Based on common questions, not patterns
No guided learning
1. Based on common questions, not patterns#
LeetCode offers over 3,000 problems, and it's impractical to practice them all. Because of this, LeetCode has created specialized lists like Blind 75 and Grind 75. The hope is that by solving a small and manageable number of problems, candidates can quickly prepare for the interview. However, these lists are largely opinionated and are limited to frequently asked problems.
The problem with these lists is that you might learn the solutions to a select group of problems, but you wouldn’t necessarily understand why those solutions work. In other words, you don’t acquire problem-solving skills to find optimal solutions for unfamiliar problems.
You may be wondering, "What's the alternative?"
An alternative approach would focus a list not on common problems, but on universal coding patterns. Coding patterns apply to a host of problems that share certain characteristics. Learning these patterns, identifying which problems can benefit from these patterns, and solving problems with these patterns truly prepares you for the unknown.
2. No guided learning#
Let’s assume that you get a list of problems to practice for your interview.
The question is: How can you learn to solve them? Having a problem in hand and a code editor is not enough. Sometimes you need some help to learn and understand the strategy behind solving those problems and, consequently, how to solve similar problems in the future.
In case you need it, a strong learning resource would provide you with additional context to guide you. It would also be able to walk you through the steps of solving a coding problem:
Understand the problem, including sample inputs and outputs.
Try to solve the problem on your own before learning an existing solution.
Try to implement the solution and test it on a number of test cases.
After attempting the problem, explore different solution approaches (e.g., naive and optimized) and strive to understand the algorithms and their implementation.
Unfortunately, there are very few platforms out there that provide such kind of learning experience.
The Educative-99 method#
Educative-99 is a curated list of 99 coding problems to help you ace your coding interview. Unlike LeetCode's lists and other LeetCode alternatives, Educative-99 consists of 99 problems that actually prepare you for thousands more.
These 99 coding problems train you on the 26 essential coding patterns that apply to countless coding problems. These patterns are the same as those taught in Educative's best-selling Grokking Coding Interview Patterns.
In addition to learning the patterns that underlie coding problems, you'll get a structured learning strategy, as well as a deeper understanding of the algorithms that are foundational to programming. Educative's learning platform is highly interactive, illustrating concepts with illustrations and letting you write and run code in your browser. In addition, you get the help of AI-assisted learning in case you need personalized feedback on improving your solutions.
Coding patterns you need to know#
Educative-99 includes all 26 coding patterns found in the Grokking Coding Interview Patterns series. These patterns are grouped into 11 categories, through which you can advance in the course across 11 hands-on modules. The details of these modules are explained below:
1. Two Pointers & Co.#
The Two Pointers & Co. module groups together three of the most common patterns that might be used to solve problems where the input data is linear: Two Pointers, Fast and Slow Pointers, and Modified Binary Search. Both the Two Pointers and the Modified Binary Search patterns derive their power from their ability to efficiently reduce the size of the search space; that is, both exemplify the divide-and-conquer approach to problem-solving. The Fast and Slow Pointers pattern may be considered a variant of the Two Pointers pattern, where the two pointers move at different speeds to traverse the input list in a way that solves the given problem.
Pattern | Description | Sample LeetCode Problems |
Two Pointers | Uses two pointers to traverse an array or list, moving them based on the conditions of the problem until they are satisfied. | |
Fast and Slow Pointers | Utilizes two pointers, one advancing faster, for efficient problem-solving in linked lists and arrays. | |
Modified Binary Search | Applies 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. |
2. Basic Data Structures#
Interviewers want to be sure that the candidate is comfortable manipulating linked lists, arrays, and data structures based on them, such as stacks and matrices. The Basic Data Structures module provides the practice we need to review the concepts and techniques needed to solve common problems involving these data structures.
Pattern | Description | Sample LeetCode Problems |
In-Place Manipulation of a Linked List | Modifies the order of a linked list by manipulating pointers without using additional data structures or memory for efficient in-place transformation. | |
Stacks | Utilizes a stack data structure for managing LIFO (Last In First Out) order to solve problems involving nested or sequential operations. | |
Matrices | Efficiently processes problems involving 2D arrays, enabling optimal manipulation and analysis of matrix data. |
3. Trees#
Trees play a pivotal role in organizing hierarchical data, optimizing searches, and solving complex problems. In the Trees module, we learn how to choose the appropriate tree for a given problem and apply the more appropriate of the two major traversal techniques, breadth-first or depth-first, to solve it.
Pattern | Description | Sample LeetCode Problems |
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. | |
Tree Breadth-First Search | Traverses a tree level by level, exploring all nodes at the present depth before moving on to nodes at the next depth. |
4. Hashing and Tracking#
Hash maps are powerful data structures that enable programmers to design more efficient solutions for certain problems. After practicing solving problems using hash maps in the Hashing and Tracking module, we move to the more general problem-solving skill of figuring out the key characteristics of a problem and using various techniques to track them to design remarkably efficient solutions for seemingly complex problems.
Pattern | Description | Sample LeetCode Problems |
Hash Maps | Utilizes a data structure that maps keys to values, enabling efficient insertion, deletion, and retrieval operations for associative arrays. | |
Knowing What to Track | Focuses on tracking specific elements or properties during iteration to solve problems effectively. |
5. Heaps#
The Heaps module teaches us the underlying concepts and equips us with the techniques needed to use heaps to efficiently solve a diverse range of problems. In many problems, we need to find the k most/least frequent or k largest/smallest elements in a given set of elements. Such problems, common in data analysis, natural language processing, online algorithms, as well as in recommendation systems, might be solved using the Top K Elements pattern. The fast insertion and deletion operations possible with heaps make them ideal for implementing efficient algorithms for such problems. Further, we can use two heaps when we need to simultaneously keep track of the k largest elements in a set, as well as the k smallest elements in the same set. Extending this idea, each heap might be based on a separate dataset. These techniques are used to solve problems categorized under the Two Heaps pattern.
Pattern | Description | Sample LeetCode Problems |
Top K Elements | Identifies and retrieves the K largest, smallest, or frequent elements from a collection, optimizing queries for specific data subsets. | |
Two Heaps | Manages two heaps, often a max-heap and a min-heap, for optimized operations like finding median efficiently in dynamic datasets or handling extreme values in datasets. |
6. Fusion#
Many information processing and decision-support problems require us to merge information coming from multiple sources to build up a global picture of a situation before making some business decisions. The Fusion module helps us understand and deploy two common coding patterns used to merge lists of structurally homogeneous data to solve hard problems such as those in scheduling, social media applications, and I/O algorithms.
Pattern | Description | Sample LeetCode Problems |
Merge Intervals | Combines overlapping or adjacent intervals in a sorted list, creating a consolidated set for simplified analysis or problem-solving. | |
K-Way Merge | Merges K sorted lists or arrays into a single sorted output, efficiently handling scenarios with multiple sorted inputs. |
7. Go with the Flow#
The optimal solution to certain problems requires examining all possibilities. If any possibility is ignored, we are unable to say with mathematical certainty that our solution is optimal. In such situations, we use a technique known as backtracking to exhaustively traverse the entire solution space and collect all the information needed to compute the optimal solution. The Subsets pattern groups together those problems where we need to systematically construct all the possible subsets of a given set or a mathematically defined subset of those subsets. Again, the technique used for the construction of such a set of subsets is, in fact, backtracking. The Go with the Flow module covers this technique in detail.
Pattern | Description | Sample LeetCode Problems |
Backtracking | Explores all possibilities to find solutions, often used in problems with multiple decision points, exploring, and undoing choices. | |
Subsets | Generates all possible subsets of a set, systematically exploring combinations for problem-solving, often using backtracking or bit manipulation. |
8. Optimization Techniques#
The Optimization Techniques module teaches us two of the most important problem-solving techniques in the optimization domain: Greedy Techniques and Dynamic Programming. Not all problems require an exhaustive search through the solution space. Rather, consistently choosing the locally optimal solution can be proven to yield the globally optimal solution. In such situations, Greedy Techniques result in simple, highly efficient solutions. Other problems have a well-defined structure, where the same subproblem is encountered and solved multiple times. Dynamic Programming techniques allow us to compute once and reuse the solutions to recurring subproblems.
Pattern | Description | Sample LeetCode Problems |
Greedy Techniques | Makes locally optimal choices at each step, aiming for a globally optimal solution in problem-solving scenarios, often without reassessment. | |
Dynamic Programming | Solves complex problems by breaking them into smaller overlapping subproblems and storing results to avoid redundant computations, optimizing time and space. |
9. Advanced Data Structures#
Proficiency in advanced data structures is a marker of a highly experienced engineer, and interviewers use such questions to fine-tune how they grade candidates. The Advanced Data Structures module teaches us how to use two advanced data structures—graphs and tries—to solve relatively complex problems. Some problems require very specific requirements that might not be handled by standard data structures. To solve these, we must gain experience in advanced skills, such as designing, implementing, and efficiently using custom data structures.
Pattern | Description | Sample LeetCode Problems |
Graphs | Uses graph data structure to represent and solve problems involving nodes and edges, enabling efficient traversal and relationship analysis. | |
Trie | Organizes data in a tree structure where each node represents a character, enabling efficient insertion, deletion, and search operations for words. | |
Custom Data Structures | Designs specialized structures tailored to specific problem requirements, optimizing data representation and operations for efficient solutions. |
10. Advanced Sorting Techniques#
The Advanced Sorting Techniques module focuses on two advanced sorting algorithms: Cyclic Sort and Topological Sort. These are often used in coding interviews as a way to distinguish between average and outstanding candidates. Cyclic Sort might only be used if the input list meets certain criteria, and in those situations, it’s the most efficient sorting algorithm possible. Topological Sort utilizes graph-based algorithms to deal with partially ordered elements. This module teaches us to recognize whether either of these advanced sorting techniques is applicable, as well as how to use them to solve the given problem.
Pattern | Description | Sample LeetCode Problems |
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. | |
Topological Sort | Determines a valid linear ordering of nodes in a directed acyclic graph that have dependencies on each other, crucial in tasks like scheduling or dependency resolution. |
11. Elegant by Design#
Each of the patterns in the Elegant by Design module exemplifies the power of dissecting a problem in detail and figuring out the least expensive operations required to solve it. In the Bitwise Manipulation pattern, we learn to leverage the power of bit-level operators to deliver more efficient solutions. With the Sliding Window pattern, we gain practice in various techniques to ensure that a solution that seems to require nested loops actually executes in linear time. Using the Union Find pattern, we learn to solve a diverse set of problems using the disjoint-set data structure.
Pattern | Description | Sample LeetCode Problems |
Bitwise Manipulation | Uses bit-level operations to solve problems by leveraging binary representations of numbers. | |
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. | |
Union Find | Maintains disjoint sets, supporting operations to determine the set to which an element belongs (find) and merge two sets (union). |
Which is the best platform?#
While you can practice to your heart's content with its pool of problems, LeetCode alone won't prepare you for problems you've never seen before.
The best use of your limited time is to focus on pattern-based interview prep, which is why Educative-99 is a game-changer for coding interview prep. Instead of bombarding you with an extensive library of problems, Educative-99 teaches the 26 coding patterns that can help you solve thousands of problems, making it one of the top LeetCode alternatives.
You can check it out in various languages below.
Good luck — and happy interviewing!
