Core Fundamentals 4,892 students 4.9/5

Java DSA Mastery

Master Data Structures and Algorithms with Java. From arrays and linked lists to trees and graphs, crack coding interviews and build efficient software with proven problem-solving techniques.

9
Modules
50+
Hours
200+
Problems
9
Assignments
Java Arrays Linked Lists Trees Graphs Algorithms
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Course Outline

1Java Fundamentals & Complexity Analysis

0/2
1.1 Java Basics & OOP
Java syntax, variables, and data types
Classes, objects, and inheritance
Polymorphism and encapsulation
Collections framework (ArrayList, HashMap, etc.)
1.2 Big O & Complexity Analysis
Time complexity and space complexity
Big O notation and asymptomatic analysis
Best, average, and worst case scenarios
Analyzing code efficiency
Assignment: Java Basics Quiz

2Arrays & Strings

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2.1 Array Operations & Techniques
Array fundamentals and indexing
Searching: Linear and binary search
Sorting: Bubble, selection, insertion sort
Two-pointer technique and sliding window
2.2 String Manipulation
String vs StringBuffer vs StringBuilder
Pattern matching and KMP algorithm
Palindromes and anagrams
Character frequency and substring problems
Assignment: Array & String Problems

3Linked Lists

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3.1 Singly & Doubly Linked Lists
Linked list structure and node creation
Insertion, deletion, and traversal
Doubly linked lists and circular lists
Fast and slow pointer technique
3.2 Linked List Problems
Detecting cycles and finding middle node
Reversing linked lists
Merging sorted lists
Complex linked list manipulation
Assignment: Linked List Implementation

4Stacks & Queues

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4.1 Stacks & Applications
Stack implementation using arrays and lists
Push, pop, peek operations
Parentheses matching and expression evaluation
Infix to postfix conversion
4.2 Queues & Priority Queues
Queue implementation (circular and linked list)
Enqueue and dequeue operations
Priority queues and heaps
Deques and real-world queue applications
Assignment: Stack & Queue Problems

5Trees & Binary Search Trees

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5.1 Tree Fundamentals
Tree structure and terminology
Binary trees and properties
Tree traversals: Inorder, preorder, postorder
Level-order traversal (BFS)
5.2 Binary Search Trees
BST properties and search operation
Insertion and deletion in BSTs
Balancing and AVL trees
Red-black trees and self-balancing
5.3 Advanced Trees
Trie data structure for string problems
Segment trees and interval trees
N-ary trees and general tree problems
Heap data structure and heapsort
Assignment: Tree Problems & Implementation

6Graphs & Graph Algorithms

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6.1 Graph Fundamentals
Graph representation: adjacency matrix and list
Directed and undirected graphs
Weighted graphs and edge properties
Graph traversals: DFS and BFS
6.2 Shortest Path Algorithms
Dijkstra's algorithm for shortest paths
Bellman-Ford algorithm
Floyd-Warshall algorithm
Applications and optimizations
6.3 Advanced Graph Algorithms
Minimum spanning trees: Kruskal and Prim
Topological sorting and DAGs
Strongly connected components
Bipartite graphs and matching
Assignment: Graph Traversal & Algorithms

7Sorting & Advanced Algorithms

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7.1 Sorting Algorithms
Merge sort and analysis
Quick sort and variations
Heap sort and counting sort
Radix and bucket sort
7.2 Divide & Conquer, Greedy
Divide and conquer paradigm
Greedy algorithms and correctness
Activity selection and Huffman coding
Interval scheduling problems
Assignment: Sorting & Algorithm Design

8Dynamic Programming & Recursion

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8.1 Recursion & Backtracking
Recursive function design and base cases
Stack overflow and tail recursion
Backtracking algorithms
N-Queens and permutation problems
8.2 Dynamic Programming
Overlapping subproblems and memoization
Tabulation and bottom-up DP
Longest subsequences and editing distance
Knapsack problems and coin change
Assignment: Recursion & DP Problems

9Real-World Projects & Interview Prep

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Project 1: Stock Trading Profit Analyzer
Array manipulation and prefix sums
Dynamic programming optimization
Real-time data handling
Project 2: LRU Cache Implementation
Doubly linked list with HashMap
O(1) operations and eviction policies
Cache optimization strategies
Project 3: Social Network Graph
Graph implementation and relationships
BFS for connection finding
Community detection and clustering
Project 4: Maze Solver with Pathfinding
BFS and DFS for pathfinding
A-star algorithm optimization
Visualization and performance analysis
Project 5: Text Editor with Undo/Redo
Stack-based command pattern
Undo and redo functionality
Memory management and optimization
Project 6: Task Scheduler
Priority queue and heap implementation
Job scheduling algorithms
Load balancing and optimization
Final Capstone & Interview Questions
What You'll Learn
  • Master core data structures from arrays to graphs
  • Analyze algorithm complexity with Big O notation
  • Solve 200+ coding problems with optimal solutions
  • Ace technical interviews at top tech companies
  • Build real-world projects with efficient implementations
  • Master dynamic programming and advanced algorithms
Quick Navigation
1. Fundamentals 2. Arrays & Strings 3. Linked Lists 4. Stacks & Queues 5. Trees 6. Graphs 7. Sorting 8. Recursion & DP 9. Projects
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