The Ultimate "JAVA in DSA" Course for beginners in Java - All important DSA Competitive Coding Patterns for Technical Interviews

 

I've gotten back into the DSA world recently, which I have to say that I really loathe. Although coding is one of my favorite hobbies and I'm pursuing what I love, I can't really say that I'm a fan of competitive coding at all.

So I still gathered all of my courage to start competitive coding in python for technical rounds in interviews, I did a few!

But the day of the interview, 3 hours before the exam, my college placement cell dropped the bomb, "The only programming languages allowed for the test are: Java, C++ or C".

And not just any version, one of the old crusty versions that they chose very specifically. I had some experience programming with all of them throughout the duration of my college-time, but after I shifted to programming professionally in python, I lost my touch.

I sat in the interview hall, and wasted 45 minutes trying to get the Java syntax right, barely solving 50% of 3 questions each. Sad, really.

SO, I plan to fix all of that. And this tutorial is meant for all of those who wish to get (secretly) ahead of everyone else in JAVA.

Course Introduction

Why Java? Why not C++? Well C++ is quite similar to Java, and with a few conceptual tweaks, you can easily migrate. Java is more widely used throughout the industry, especially if you want a future in a majority of the available software development roles.

But enough about that. Let's start diving into Java. I've structured this post very neatly so that you don't get overwhelmed, yet still know everything there is to know about diving into competitive coding, and no this course is not meant for Java development at all.

What does Java look like?

This program demonstrates fundamental Java syntax elements like:

Comments: How to add single-line (//) and multi-line (/* ... */) comments to explain your code.

Class Structure: The basic structure of a Java class (public class JavaBasics).

main Method: The entry point of a Java application (public static void main(String[] args)).

Output: How to print text to the console using System.out.println() and System.out.print().

Variables and Data Types: Declaring and using different types of variables (int, double, char, boolean, String).

Arithmetic Operations: Performing basic math operations (+, -, *, /).

Constants: Using the final keyword to declare a constant.

You can copy this code into a Java IDE (like IntelliJ IDEA or Eclipse) or a simple text editor, save it as JavaBasics.java, compile it using javac JavaBasics.java, and then run it using java JavaBasics from your terminal to see the output.

// This is a single-line comment. It explains the code on a single line.

/*

 * This is a multi-line comment.

 * It can span multiple lines and is often used for more detailed explanations

 * or for commenting out larger blocks of code.

 */

// Every Java application must have at least one class.

// The class name should ideally start with an uppercase letter.

public class JavaBasics {

    // The 'main' method is the entry point of any Java application.

    // When you run a Java program, the Java Virtual Machine (JVM) looks for this method.

    public static void main(String[] args) {

        // --- Output to the Console ---

        // System.out.println() is used to print text to the console, followed by a new line.

        System.out.println("Hello, Java Learners!"); // Prints a string literal

        // System.out.print() is similar but does not add a new line at the end.

        System.out.print("This is a basic ");

        System.out.println("Java program."); // This will appear on the same line as the previous print.

        // --- Variables and Data Types ---

        // Variables are containers for storing data values.

        // You must declare the data type of a variable when you declare it.

        // Integer (int): Stores whole numbers (without decimals).

        int age = 30; // Declares an integer variable named 'age' and assigns it the value 30.

        System.out.println("My age is: " + age); // Concatenating a string with an integer variable.

        // Double: Stores floating-point numbers (numbers with decimals).

        double price = 19.99; // Declares a double variable named 'price'.

        System.out.println("The price is: $" + price);

        // Character (char): Stores a single character. Enclosed in single quotes.

        char grade = 'A'; // Declares a char variable named 'grade'.

        System.out.println("My grade is: " + grade);

        // Boolean (boolean): Stores true or false values.

        boolean isJavaFun = true; // Declares a boolean variable.

        System.out.println("Is Java fun? " + isJavaFun);

        // String: Stores sequences of characters (text). Enclosed in double quotes.

        // String is a class, not a primitive data type, but it's used very commonly.

        String greeting = "Welcome to Java!"; // Declares a String variable.

        System.out.println(greeting);

        // --- Basic Arithmetic Operations ---

        int num1 = 10;

        int num2 = 5;

        int sum = num1 + num2; // Addition

        System.out.println("Sum: " + sum);

        int difference = num1 - num2; // Subtraction

        System.out.println("Difference: " + difference);

        int product = num1 * num2; // Multiplication

        System.out.println("Product: " + product);

        int quotient = num1 / num2; // Division (integer division, result is a whole number)

        System.out.println("Quotient: " + quotient);

        // --- Constants (using 'final' keyword) ---

        // The 'final' keyword makes a variable a constant, meaning its value cannot be changed once assigned.

        final double PI = 3.14159; // Declares a constant PI.

        System.out.println("Value of PI: " + PI);

        // PI = 3.14; // This line would cause a compile-time error because PI is final.

        // End of the main method.

    }

    // End of the JavaBasics class.

}

DSA Roadmap

Now, onto the actual struggle, the part where you'll spend the next few months trying to climb the Leetcode ladder.

To ace tough technical coding rounds, you'll need a strong grasp of various Data Structures and Algorithms. Here's a comprehensive list of topics to cover:

I. Core Data Structures:

Arrays:

• Basic operations (access, insert, delete, update)
• Dynamic arrays (ArrayList in Java)
• Multi-dimensional arrays
• Prefix sums, sliding window, two pointers

Strings:

• String manipulation (substrings, concatenation, searching)
• Character arrays
• String builders/buffers
• Pattern matching (KMP, Rabin-Karp - optional for most interviews but good to know)

Linked Lists:

• Singly, Doubly, and Circular Linked Lists
• Operations: insertion, deletion, traversal, reversal, finding middle, cycle detection (Floyd's Tortoise and Hare)

Stacks:

• LIFO (Last-In, First-Out) principle
• Operations: push, pop, peek, isEmpty
• Applications: expression evaluation, balanced parentheses, next greater element
• Queues:

• FIFO (First-In, First-Out) principle
• Operations: enqueue, dequeue, peek, isEmpty
• Types: Deque, Priority Queue
• Applications: BFS, task scheduling

Trees:

• Binary Trees: Traversal (Inorder, Preorder, Postorder, Level Order/BFS), height, diameter, lowest common ancestor (LCA)
• Binary Search Trees (BSTs): Insertion, deletion, searching, validation, converting sorted array to BST
• Heaps: Min-Heap, Max-Heap, heapify, building a heap, applications (Priority Queue, Kth largest/smallest element)
• Balanced BSTs (AVL, Red-Black Trees): (Good to know concepts, less likely to implement from scratch in interviews)
• Tries (Prefix Trees): For string-related problems, autocomplete

Graphs:

• Representations: Adjacency Matrix, Adjacency List
• Traversal: Breadth-First Search (BFS), Depth-First Search (DFS)
• Topological Sort (for Directed Acyclic Graphs - DAGs)
• Minimum Spanning Trees (MST): Prim's, Kruskal's (concepts, less common to implement)
• Shortest Path Algorithms: Dijkstra's, Bellman-Ford (concepts, less common to implement), Floyd-Warshall (for all-pairs shortest path)
• Cycle Detection
• Hash Tables (Hash Maps/Sets):
• Hashing, collision resolution
• Operations: insert, delete, search
• Applications: frequency counting, caching, checking for duplicates, two-sum problem variations

II. Core Algorithms:

Sorting Algorithms:

• Bubble Sort, Selection Sort, Insertion Sort (for understanding basics)
• Merge Sort, Quick Sort (Divide and Conquer, crucial for interviews)
• Heap Sort
• Counting Sort, Radix Sort (for specific constraints)

Searching Algorithms:

• Linear Search
• Binary Search (on sorted arrays, rotated sorted arrays, finding boundaries)
• Recursion and Backtracking:
• Understanding base cases and recursive steps
• Solving problems like permutations, combinations, subsets, N-Queens, Sudoku Solver

Dynamic Programming (DP):

• Identifying overlapping subproblems and optimal substructure
• Memoization (top-down) vs. Tabulation (bottom-up)
• Common patterns: Fibonacci, knapsack, longest common subsequence/substring, coin change, unique paths

Greedy Algorithms:

• Making locally optimal choices to achieve a global optimum
• Applications: activity selection, coin change (sometimes), Huffman coding
• Bit Manipulation:
• Bitwise operators (&, |, ^, ~, <<, >>, >>>)
• Checking/setting/clearing bits, counting set bits, power of 2, single number problems

III. Problem-Solving Techniques & Concepts:

1. Time and Space Complexity Analysis (Big O Notation):

Crucial for evaluating algorithm efficiency. You must be able to analyze and discuss this for your solutions.

2. Two Pointers:

Efficiently traversing arrays/linked lists.

3. Sliding Window:

For problems involving contiguous subarrays/substrings.

4. Divide and Conquer:

Breaking down problems into smaller subproblems.

5. Mathematical Concepts:

Basic number theory (prime numbers, GCD, LCM)

Modular arithmetic

Combinatorics (permutations, combinations)

IV. System Design (for Senior Roles):

While not strictly DSA, for more senior roles, you'll also encounter system design questions. These involve designing scalable, reliable, and maintainable systems. Key topics include:

Scalability (vertical vs. horizontal)

Load balancing

Databases (SQL vs. NoSQL)

Caching

Message queues

APIs and Microservices

Concurrency and distributed systems

How to Prepare:

Understand the fundamentals: Don't just memorize solutions; understand why an algorithm works.

Practice, practice, practice: LeetCode, HackerRank, AlgoExpert, etc., are your best friends, or your worst enemies. Start with easy, then medium, then hard, or follow a practice sheet like the Strivers Sheet.

Solve problems by topic: Focus on one data structure or algorithm at a time until you feel comfortable.

Mock interviews: Practice explaining your thought process and solutions aloud.

Review: Go over solutions, even if you solved the problem, to see if there are more optimal approaches.

Data Structures and Algorithms (DSA) and LeetCode problems often fall into identifiable patterns. Recognizing these patterns is key to efficiently solving a wide range of problems. 

Here's a detailed list of primary patterns you'll encounter, along with examples of when they are typically used:

I. Core Algorithmic Paradigms & Techniques:

Two Pointers:

Description: Uses two pointers (indices) that move through an array or list, often in the same direction or opposite directions. This technique is highly efficient for problems requiring comparisons or interactions between elements.

Variants:

Same-Direction Pointers: One pointer might move faster than the other, or both move at a controlled pace. Useful for problems like finding duplicates, removing elements in-place, or sliding window problems.

Opposite-Direction Pointers: Pointers start at opposite ends and move towards the center. Ideal for problems involving sorted arrays, finding pairs, or partitioning.

Common Use Cases:

Finding pairs with a certain sum in a sorted array.

Removing duplicates from a sorted array.

Reversing an array or string in-place.

Finding the middle of a linked list (fast and slow pointers).

Cycle detection in linked lists (Floyd's Tortoise and Hare).

Container with most water.

Sliding Window:

Description: Used on arrays or strings to find a sub-array or sub-string (a "window") that satisfies a certain condition. The window "slides" over the data, expanding or shrinking as needed.

Variants:

Fixed-Size Window: The window size remains constant.

Dynamic-Size Window: The window expands or shrinks based on conditions.

Common Use Cases:

Finding the maximum/minimum sum subarray of a given size.

Finding the longest substring with K distinct characters.

Smallest subarray with a given sum.

Longest substring without repeating characters.

Permutation in string.

Fast & Slow Pointers (Floyd's Tortoise and Hare):

Description: Two pointers moving at different speeds through a sequence (typically a linked list or array). Primarily used for cycle detection and finding specific points within a sequence.

Common Use Cases:

Detecting cycles in a linked list.

Finding the starting node of a cycle in a linked list.

Finding the middle of a linked list.

Happy Number problem.

Merge Intervals:

Description: Deals with problems involving a list of intervals where you need to perform operations like merging overlapping intervals, finding intersections, or inserting a new interval. Often requires sorting the intervals first.

Common Use Cases:

Merging overlapping intervals.

Inserting a new interval into a list of intervals.

Meeting rooms problem.

Interval intersection.

Cyclic Sort:

Description: Used when you have an array containing numbers in a given range (e.g., 1 to N) and need to place each number at its correct index. It's efficient for finding missing or duplicate numbers.

Common Use Cases:

Find the missing number.

Find all duplicate numbers.

Find the smallest missing positive number.

Find the first K missing positive numbers.

In-place Reversal of a Linked List:

Description: Modifying the next pointers of nodes to reverse the order of nodes within a linked list without using extra space.

Common Use Cases:

Reversing a linked list.

Reversing a sub-list of a linked list.

Reversing every K-elements sub-list.

Tree BFS (Level Order Traversal):

Description: Uses a queue to traverse a tree level by level. All nodes at the current depth are visited before moving to the next depth.

Common Use Cases:

Finding the maximum depth/height of a tree.

Finding the level with the maximum sum.

Checking if a tree is balanced.

Connecting nodes at the same level.

Tree DFS (Depth First Search):

Description: Uses recursion (or an explicit stack) to traverse a tree by exploring as far as possible along each branch before backtracking.

Variants:

Preorder: Root, Left, Right

Inorder: Left, Root, Right (produces sorted output for BST)

Postorder: Left, Right, Root

Common Use Cases:

Finding the path sum.

Checking if a tree is symmetric.

Tree diameter.

LCA (Lowest Common Ancestor).

Two Heaps (Priority Queues):

Description: Involves using a Min-Heap and a Max-Heap to efficiently solve problems where you need to maintain a relative ordering or find elements like the median. One heap stores smaller elements, the other stores larger elements.

Common Use Cases:

Finding the median of a number stream.

Sliding window median.

Capital Allocation (Maximal Capital).

Subsets / Backtracking (BFS or DFS for permutations/combinations):

Description: Used to find all possible combinations or permutations of a given set of elements. Often implemented with recursion.

Common Use Cases:

Finding all subsets of a set.

Finding all permutations of a string/array.

Combinations sum.

Generating parentheses.

N-Queens problem.

Sudoku Solver.

Modified Binary Search:

Description: While basic binary search finds an exact element, many problems require adaptations like finding the first/last occurrence, ceiling/floor, or searching in a rotated sorted array.

Common Use Cases:

Search in rotated sorted array.

Finding the ceiling/floor of a number.

Finding the first/last position of an element.

Finding an element in an array with unknown size.

Minimizing/maximizing a value where the search space is monotonic (e.g., finding the Kth smallest element in a matrix).

Top K Elements (Heap/Quickselect):

Description: Efficiently finding the K largest/smallest elements, or the Kth largest/smallest element in a given set.

Common Use Cases:

Kth largest/smallest element.

Top K frequent elements.

K closest points to origin.

Connect Ropes (using a min-heap).

K-way Merge:

Description: Problems where you need to merge K sorted lists or arrays into one sorted list/array. Often uses a Min-Heap to efficiently pick the next smallest element from all K lists.

Common Use Cases:

Merge K sorted lists.

Kth smallest element in M sorted lists.

Smallest range containing elements from K lists.

Topological Sort:

Description: For Directed Acyclic Graphs (DAGs), it's a linear ordering of vertices such that for every directed edge U -> V, vertex U comes before V in the ordering. Usually implemented with BFS (Kahn's algorithm) or DFS.

Common Use Cases:

Course Schedule.

Alien Dictionary.

Build Order.

Graph Traversal (BFS/DFS):

Description: Beyond trees, BFS and DFS are fundamental for exploring graphs. BFS is good for shortest paths on unweighted graphs and finding connected components. DFS is good for pathfinding, cycle detection, and topological sorting.

Common Use Cases:

Shortest path in unweighted graphs.

Number of islands.

Bipartite graph check.

Detecting cycles in graphs.

Finding all paths from source to target.

Dynamic Programming (DP):

Description: Solves problems by breaking them down into smaller overlapping subproblems and storing the results of these subproblems to avoid recomputing them.

Variants:

Memoization (Top-Down): Recursive approach with caching.

Tabulation (Bottom-Up): Iterative approach building up the solution.

Common Use Cases:

Fibonacci sequence (classic example).

Knapsack problem (0/1, unbounded).

Longest Common Subsequence/Substring.

Coin Change.

Edit Distance.

Unique Paths.

Climbing Stairs.

House Robber.

II. Other Important Considerations:

Bit Manipulation:

Description: Leveraging bitwise operators to solve problems efficiently, often related to numbers, sets, or flags.

Common Use Cases:

Checking if a number is even/odd.

Swapping numbers without a temporary variable.

Finding the single non-repeating number.

Power of 2.

Counting set bits.

Greedy Algorithms:

Description: Making locally optimal choices at each step with the hope of finding a global optimum. Not always guaranteed to work, but very efficient when applicable.

Common Use Cases:

Activity selection problem.

Coin change (sometimes).

Fractional Knapsack.

Minimum number of platforms.

Union-Find (Disjoint Set Union - DSU):

Description: A data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. Used for problems involving connected components in graphs or grouping elements.

Common Use Cases:

Detecting cycles in an undirected graph.

Connecting components in a graph.

Number of provinces.

Redundant connection.

By understanding these patterns, you can categorize a new problem, recall the relevant algorithms, and apply the pattern's structure to develop a solution much more quickly than starting from scratch. 

So first let's master these important patterns using Java, and then we'll apply them to the leetcode questions. This way, there will be no stopping you from acing any interview. The syntax though, is on you. So practice writing code as much as possible.

Good luck!