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Skilled Using Java

Core OOP Principles – Inheritance (Types: Single, Multilevel, Hierarchical; Using super and method overriding), Polymorphism (Compile-Time Polymorphism: Method Overloading, Runtime Polymorphism: Method Overriding and dynamic method dispatch), Encapsulation (Access Modifiers: Public, Private, Protected, Default; Getter and Setter Methods with examples), Abstraction (Abstract Classes and Interfaces; Multiple inheritance via interfaces); Advanced OOP Features - Nested Classes (Inner, Static Nested, and Anonymous Classes), Anonymous Objects and Lambda Expressions (Functional interfaces and lambda integration with collections), Enums and Annotations (Enum constants, advanced enum methods, and custom annotations); Generics in Java, Generic methods and classes, Usage with Collection Framework; Introduction to Collection Framework, Definition, hierarchy, and advantages, Core Interfaces – Collection, List (Ordered collections (e.g., ArrayList, LinkedList, Vector)), Set (Unique elements (e.g., HashSet, LinkedHashSet, TreeSet)), Queue (FIFO and Deque implementations (e.g., PriorityQueue, ArrayDeque)), Map (Key-value pairs (e.g., HashMap, LinkedHashMap, TreeMap, Hashtable)); Utility Classes – Collections (Sorting, searching, and manipulating collection elements), Arrays (Utility methods for arrays); Iterators (Iterator and ListIterator), Streams API, Filtering, mapping, and reducing operations; Integration with lambda expressions.

Understanding Asymptotic Notations - Big O Notation (O) - Upper bound of algorithm complexity, Big Omega Notation (Ω) - Lower bound of algorithm complexity, Big Theta Notation (Θ) - Tight bound both upper and lower; Common Complexities - O(1): Constant time examples (e.g., accessing array elements), O(n): Linear time examples (e.g., single loop iterations), O(log n): Logarithmic time examples (e.g., binary search), O(n²): Quadratic time examples (e.g., nested loops). Exercises - Analyze the time complexity of Java code snippets (Iterative loops, Recursive methods, Nested loop structures); Measuring Time Complexity in Java - Using System.nanoTime() or System.currentTimeMillis() to measure execution time, Analyze time complexity of search and sort algorithms (e.g., linear search, binary search, bubble sort); Space Complexity, Static vs. Dynamic Memory Allocation, Static Memory: Examples with fixed-size data structures (e.g., arrays), Dynamic Memory: Examples with dynamic data structures (e.g., linked lists); Stack and Heap Memory in Java - Difference between stack and heap memory, Function call stack and local variables, Heap memory for objects and dynamic allocation; Measuring Space Complexity in Java - Estimating memory usage of objects and data structures, Analyzing memory usage for recursive functions; Optimizing Time and Space Complexity - Use of efficient data structures (e.g., HashMap vs. ArrayList), Code refactoring to reduce redundant computations, Tail recursion optimization.

Introduction to Array, Definition and importance of array in data structures, Declaring, initializing, and accessing arrays in Java; Array Operations - Traversal: Iterating through array elements, Insertion: Adding elements at specific positions, Deletion: Removing elements from specific positions, Searching: Locating elements using indexes or values; Multidimensional Arrays - Concept of 2D arrays and their applications (e.g., matrices), Declaring and initializing 2D arrays in Java, Basic operations on matrices (e.g., addition, subtraction); Sorting Algorithms - Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, Searching Algorithms - Linear Search: Iterative approach for unsorted arrays, Binary Search: Optimized searching for sorted arrays; Introduction to Strings, String Class: Creating and manipulating strings, StringBuilder and StringBuffer: Differences between the two, Efficient string manipulation using mutable classes; Common String Operations - Extracting substrings, Reversing strings, Comparing strings (equality, lexicographical order), Concatenation and splitting strings; Pattern Matching Algorithms - Brute Force Algorithm, Knuth-Morris-Pratt (KMP) Algorithm, Simplified explanation of prefix-suffix matching, Practical implementation in Java.

Introduction to Linked List - Concept of dynamic data structures and the need for linked list, Types of linked lists: Singly Linked List, Doubly Linked List, Circular Linked List, Operations on Linked List - Insertion: Adding nodes at the beginning, end, and specific positions, Deletion: Removing nodes from the beginning, end, and specific positions, Search: Finding elements by value or position, Traversal: Iterating through linked list elements; 1.3 Applications of Linked Lists (Implementation of dynamic memory allocation, Use in polynomial arithmetic, Simulation of real-world scenarios like train scheduling); Introduction to Stack - Definition and LIFO (Last In First Out) principle, Stack representation using arrays and linked lists; Operations on Stack – Push Adding elements to the stack, Pop: Removing elements from the stack, Peek/Top: Accessing the top element of the stack; Balancing Parentheses: Checking valid combinations of brackets, Infix to Postfix Conversion, Steps and algorithm with examples; Using stacks for postfix expression evaluation; Introduction to Queues, FIFO (First In First Out) principle, Queue representation using arrays and linked lists; Types of Queues - Simple Queue, Circular Queue, Deque (Double-Ended Queue), Priority Queue, Applications of Queues (Process scheduling in operating systems., Handling requests in web servers, Data streaming and buffering).

Introduction to Tree, characteristics of tree, Terminology: Node, root, leaf, height, depth, level, siblings, degree; Types of trees: General trees, binary trees, binary search trees, balanced trees; Binary Tree - Structure and properties of binary trees, Traversals - In-order Traversal (Left-Root-Right), Pre-order Traversal (Root-Left-Right), Post-order Traversal (Left-Right-Root), Level-order Traversal (BFS-based level-wise traversal); Binary Search Trees (BST) - Insertion, deletion, and search operations; Applications: Database indexing, dynamic sets; Balanced Trees - AVL Trees (Definition and rotations (left, right, left-right, right-left), Insertion and balancing examples), Red-Black Trees (Properties and color rules, Insertion and rebalancing techniques); Introduction to Heap, Applications of heap, Difference between min-heap and max-heap; Heap Operations - Insertion: Maintaining the heap property, Deletion: Removing the root and rebalancing the heap; Heap Sort - Concept of sorting using heaps, Implementation steps: Build heap, extract elements, and adjust heap; Introduction to Graphs, Representation of Graphs - Adjacency Matrix (2D array representation, Advantages and disadvantages); Adjacency List (Linked list representation, Space-efficient for sparse graphs); Graph Traversal Algorithms - Breadth-First Search (BFS) - Level-order exploration of nodes, Implementation using queue, Depth-First Search (DFS) - Recursive and iterative implementations, Applications: Cycle detection, connected components; Shortest Path Algorithms - Dijkstra’s Algorithm (Single-source shortest path, Use of priority queues for optimization), Floyd-Warshall Algorithm (All-pairs shortest path algorithm, Dynamic programming approach).

Introduction to Hashing, Importance of hashing in data structures, Use cases: Fast data retrieval, indexing, and caches; Hash Tables : Concept and working of hash functions, Implementation in Java using arrays; Hash Maps: Built-in Java implementation (HashMap<K, V>), Operations: put(), get(), remove(), containsKey(); Collision Resolution Techniques – Chaining (Handling collisions with linked lists, Implementation and analysis), Open Addressing (Linear Probing: Finding the next available slot, Quadratic Probing: Avoiding primary clustering, Double Hashing: Using a secondary hash function); Implementation of hash-based sets (HashSet), Real-world applications: Caches, load balancing, and dictionaries; Introduction to Recursion and key concepts (base case, recursive step), Stack memory usage in recursive calls; Recursive Algorithms - Factorial Calculation: Explanation and Java implementation, Fibonacci Series: Recursive and optimized approaches, Tower of Hanoi: Recursive solution and explanation; Introduction to Backtracking - Concept of exploring all possibilities and backtracking when necessary, Use cases of backtracking in problem-solving; Backtracking Problems - N-Queens Problem (Placing N queens on an NxN chessboard without conflicts, Implementation with recursive function), Sudoku Solver (Recursive approach to fill empty cells, Constraint checking for valid placements), Maze Solving (Finding paths in a maze using backtracking, Representation of the maze and implementation in Java).

Introduction to Divide and Conquer Algorithms,     Importance of divide and conquer, Steps in divide and conquer: Divide, Conquer, and Combine, Advantages and real-world applications of divide and conquer; Quick Sort - Algorithm and working principle of quick sort, Implementation in Java: Partitioning logic (Lomuto and Hoare partition schemes), Recursive quick sort implementation; Time complexity analysis (average and worst cases); Merge Sort - Algorithm and working principle of merge sort, Implementation in Java: Dividing the array into halves, Merging sorted halves, Comparison with quick sort, Time complexity analysis.

Definition and importance of dynamic programming, Characteristics of DP problems (Overlapping subproblems, Optimal substructure), Difference between dynamic programming and divide and conquer, Approaches (Top-down (memoization), Bottom-up (tabulation)); Solving Problems Using DP, Longest Common Subsequence (LCS) : Problem definition and examples, Recursive solution with memorization, Tabulation approach in Java; Knapsack Problem: 0/1 Knapsack problem: Problem definition and examples, Recursive solution and tabulation method in Java; Additional Examples: Fibonacci sequence using DP, Minimum number of coins for change.

Introduction to Disjoint Sets, Importance of disjoint set data structure, Applications in graph algorithms and network connectivity, Basic operations (find: Determining the representative of a set, union: Merging two sets); Union by Rank (Merging sets based on rank (or depth), Optimizing union operations); Path Compression (Flattening the structure of trees during find operation, Reducing time complexity), Implementation of optimized union-find in Java; Applications of Disjoint Sets - Kruskal’s Algorithm (Minimum spanning tree construction, Step-by-step implementation using union-find), Connected Components in a Graph (Finding the number of connected components in an undirected graph, Practical implementation with adjacency lists);  Principles of Greedy Algorithms, key characteristics (Greedy choice property, Optimal substructure), Difference between greedy and dynamic programming approaches; Applications of Greedy Algorithms - Huffman Encoding, Activity Selection Problem, Minimum Spanning Trees (Prim’s Algorithm, Kruskal’s Algorithm).

Memory structure in Java: Method area, heap, stack, PC register, native method stack, Memory allocation for objects and variables; Stack Memory (Characteristics, function calls, local variables, and scope), Heap Memory (Characteristics, dynamic memory allocation, and garbage collection), Practical examples to demonstrate stack and heap usage; Garbage Collection Mechanisms in Java : Definition and importance of garbage collection, Types of garbage collectors: Serial, Parallel, CMS, and G1; Garbage collection process and phases: Mark, Sweep, Compact; How to invoke garbage collection manually (System.gc() and finalize()); Tools for garbage collection analysis: VisualVM and JConsole.

Definition and benefits of immutability (thread safety, predictability), Comparison with mutable structures; Examples of Immutable Data Structures - Implementing immutable classes in Java (Rules: Final class, private fields, no setter methods, defensive copying, Example: Immutable linked lists), Usage in functional programming paradigms; Concurrency in Data Structures - Introduction to Multithreading, Threads and processes in Java, Creating threads: Extending Thread class vs. implementing Runnable, Synchronization basics to prevent race conditions; Thread-Safe Data Structures, ConcurrentHashMap (Internal structure and segment-based locking, Examples of usage in multi-threaded environments), CopyOnWriteArrayList (Concept and use cases for read-heavy operations), Java's Concurrency Utilities, ExecutorService: Thread pool management and task submission, Semaphore: Managing access to critical sections using permits, Practical examples for concurrency utilities.

What is caching?, Benefits of caching in applications (performance, reduced latency), Types of caching: In-memory, distributed; Designing an LRU Cache - Least Recently Used (LRU) cache concept, Using Java data structures: LinkedHashMap: Built-in features for LRU implementation, Doubly Linked List with HashMap: Custom implementation of LRU cache, Practical implementation of an LRU cache in Java.