Full Stack Development

Prepares you for roles including

• Software Engineer, Java/Full Stack/Backend/Python/C++ Developer
• Algorithm Engineer, Software Development Engineer, AI/ML Engineer, Data Engineer
• Embedded Software Engineer, Competitive Programmer
• Technical interviews at product-based software companies

Program architecture (17 phases)

• Phases 1–2 (Wk 1–3): Programming, problem solving, complexity analysis
• Phases 3–6 (Wk 4–8): Arrays, strings, linked lists, stack, queue, hashing
• Phases 7–10 (Wk 9–17): Recursion, backtracking, trees, heaps, graphs
• Phases 11–14 (Wk 18–23): Sorting, searching, greedy, dynamic programming
• Phases 15–17 (Wk 24–26): Advanced algorithms, competitive programming, interviews & capstone

Module 01 — Programming Fundamentals

• Java primary (C++/Python secondary): variables, types, operators, conditions, loops, functions, arrays, strings, I/O

Module 02 — Computational Thinking

• Problem → Input → Processing → Output; constraints, edge cases, decomposition, brute-force, optimize

Module 03 — Problem-Solving Methodology

• Understand → examples → constraints → brute-force → analyse complexity → optimize → code → test → review

Module 04 — Big-O Analysis

• Time: O(1), O(log n), O(n), O(n log n), O(n²), O(2ⁿ), O(n!)
• Space: auxiliary, input, recursion stack

Module 05 — Complexity Optimization

• Compare O(n²) vs O(n log n) vs O(n); trade memory for speed

Module 06 — Arrays

• Static/dynamic arrays, traversal, insertion, deletion, prefix sums
• Patterns: two pointers, sliding window, frequency counting, partitioning

Module 07 — Array Problem Solving

• Max subarray, duplicates, rotation, merge sorted, missing number, subarray/interval problems

Module 08 — Strings

• Manipulation, frequency maps, palindromes, anagrams, pattern matching
• Advanced awareness: string hashing, KMP, trie

Module 09 — Linked Lists

• Singly, doubly, circular; insert, delete, search, reverse, traverse

Module 10 — Linked List Algorithms

• Reverse, cycle detection, middle, merge, duplicates, intersection, palindrome
• Project: Custom Linked List Library

Module 11 — Stack

• Expression evaluation, parentheses, undo, call stack
• Next greater element, valid parentheses, min stack, infix/postfix

Module 12 — Queue

• Simple, circular, deque, priority queue; scheduling, buffering, BFS, task processing

Module 13 — Hash Tables

• Hash function, collisions, chaining, open addressing; Java HashMap, HashSet

Module 14 — Hashing Patterns

• Two Sum, frequency counting, group anagrams, longest consecutive sequence
• When hashing reduces O(n²) to O(n)

Module 15 — Recursion

• Base condition, call stack, divide and conquer; factorial, Fibonacci, tree traversal, binary search

Module 16 — Backtracking

• Choose → Explore → Undo; permutations, combinations, subsets, N-Queens, Sudoku

Module 17 — Binary Trees

• Root, parent, child, leaf, height, depth; preorder, inorder, postorder, level order

Module 18 — Binary Search Trees

• Search, insert, delete; balanced trees, height, complexity

Module 19 — Advanced Trees

• AVL, red-black, segment trees, Fenwick trees, trie
• Project: File-system / directory indexing system

Module 20 — Heap

• Min/max heap, insert, delete, heapify, extract; scheduling, top-K, median

Module 21 — Heap Algorithms

• K largest/smallest, Kth largest, merge K sorted lists, running median

Modules 22–23 — Graph Fundamentals & Traversal

• Adjacency matrix/list; directed, undirected, weighted graphs
• BFS (queue), DFS (stack/recursion); components, islands, cycle detection, path finding

Modules 24–26 — Shortest Path, MST & Advanced

• Dijkstra, Bellman-Ford, Floyd-Warshall; Kruskal, Prim, Union-Find
• Topological sort, SCC, bipartite, network flow awareness
• Project: Route Optimization System

Module 27 — Basic Sorting

• Bubble, selection, insertion — implement and analyse limitations

Module 28 — Efficient Sorting

• Merge, quick, heap sort; time, space, stability, in-place behaviour

Module 29 — Non-comparison Sorting

• Counting sort, radix sort, bucket sort

Module 30 — Searching

• Linear O(n), binary O(log n); binary search on answer, rotated arrays, monotonic functions

Module 31 — Search Problems

• First/last occurrence, search rotated array, search range, min/max feasible value

Module 32 — Greedy Strategy

• Activity selection, fractional knapsack, job scheduling, interval scheduling, minimum platforms

Module 33 — Greedy vs Dynamic Programming

• When greedy works vs when it fails

Module 34 — DP Fundamentals

• Overlapping subproblems, optimal substructure, state, transition, base case

Modules 35–36 — 1D & 2D DP

• Fibonacci, climbing stairs, house robber, coin change
• Grid paths, 0/1 knapsack, LCS, edit distance

Module 37 — Advanced DP

• Interval DP, tree DP, bitmask DP, digit DP awareness
• Project: Resource Optimization Engine

Module 38 — Divide & Conquer

• Merge/quick sort, binary search, recurrence relations

Module 39 — Advanced Techniques

• Bit manipulation, prefix sums, difference arrays, monotonic stack/queue, sweep line awareness

Module 40 — String Algorithms

• KMP, Rabin-Karp, trie, string hashing

Modules 42–44 — Interview Prep

• Explain: problem, approach, alternatives, complexity, edge cases, code, testing
• Patterns: two pointers, sliding window, BFS/DFS, backtracking, heap, greedy, DP, prefix sum, monotonic stack
• 5 mock interview rounds: fundamentals, DS, algorithms, optimization, explanation

Real-world algorithm applications

• E-commerce search/ranking, banking/fraud graphs, healthcare scheduling, logistics routing, social graphs, AI optimization

• Option 1 — Delivery Route Optimizer: locations, road network, shortest path
• Option 2 — E-commerce Search Engine: hashing, trees, trie, sorting, ranking
• Option 3 — Social Network Analytics: BFS, DFS, recommendations

• Option 4 — Hospital Resource Optimization: greedy, priority queues, DP
• Option 5 — AI Recommendation Engine: hashing, similarity, graph relationships
• Option 6 — Smart Factory Scheduler: connects with Industrial Automation

Four DSA labs

• Coding Lab (Java/C++/Python), Algorithm Visualization, Problem-Solving Lab (10–20 weekly), Interview Lab

Rasa DSA Problem Bank — 600+ problems

• Beginner 100, Intermediate 150, Advanced 100, Interview 150, Competitive 100 — by topic, difficulty, pattern

15+ portfolio projects

• Custom ArrayList, linked list, stack, queue, HashMap, sorting/search engines, tree index, graph navigator, route optimizer, scheduling engine, recommendation engine, capstone system

Three RASA certification levels

• Level 1 — DSA Foundation Professional: programming + complexity + arrays + strings + linked lists + stack + queue + hashing
• Level 2 — Professional Algorithm Engineer: trees + graphs + heaps + sorting + searching + greedy + DP
• Level 3 — Advanced Algorithms & Problem-Solving Professional: advanced algorithms + competitive programming + real-world engineering

Assessment framework (100%)

• Programming + Complexity (10%), Arrays + Strings + Linked Lists (15%), Stack + Queue + Hashing (10%), Recursion + Backtracking (10%), Trees + Heaps (15%), Graphs (15%), Sorting + Searching (5%), Greedy + DP (15%), Advanced Algorithms (5%), Capstone + Coding Assessment (10%)