Write clean, efficient code using core DSA patterns in Python or C++ (learner’s choice).

Choose the right data structure/algorithm for real problems and justify trade-offs.

Analyze time and space using Big-O and optimize solutions iteratively.

Build and ship micro-products (CLI tools, libraries, APIs) that can be monetized.

Solve interview-style problems with structured thinking and clarity.

Maintain a portfolio (GitHub) showcasing solved problems and capstone work.

Duration: 4 Months (16 Weeks)

Load: ~8–10 hrs/week (2 weekday evenings × 2 hrs + Sat/Sun lab 3–4 hrs)

Mode: Blended (in-person/online), bilingual support (English + Hindi/Marathi as needed)

Track Choice: Python or C++ from Week 1 (Java optional add-on)

Labs & Tools: VS Code, GCC/Clang or Python 3.x, Git & GitHub, unit testing, simple profiling

Assessment: Quizzes (20%), Labs (30%), Projects (40%), Participation (10%)

Prerequisites: Basic computer usage; prior coding is helpful but not mandatory

Flipped micro-lectures: short, sharp concept drops; class time = practice.

Problem-of-the-Day: warm-up puzzles to build rhythm.

Pair programming & code reviews: learn to reason, argue, and improve.

Pattern-first approach: master templates (two-pointers, sliding window, DP, etc.).

Weekly micro-project: apply concepts to small, useful tools.

Show & ship: push to GitHub every week, present in lightning demos.

Interview drills: timed practice + whiteboard walkthroughs.

Freelancing: Build utilities (data parsers, optimizers, CLI tools), charge per project/feature.

Product gigs: Contribute to startups/SMEs—faster code = lower cloud bills.

Teaching/tutoring: Run local DSA clubs, paid workshops for school/college students.

Competitive placements: Strong DSA is still the core filter in many Indian tech interviews.

Entrepreneurship: Package your algorithms as micro-SaaS or libraries (billing, routing, scheduling).

Variables, data types, I/O; functions; debugging; CLI basics; Git/GitHub.

Big-O intuition; benchmarking simple loops.

Lab: Build a “Time-It” utility to compare naive vs optimized loops.

Loops, conditionals, recursion vs iteration; call stack.

Pattern: Two-Pointers, Sliding Window (intro).

Lab: Subarray sums, longest unique substring (string/window practice).

One-D, Two-D arrays, matrix traversal; string manipulation; hashing basics.

Lab: Matrix spiral, frequency counter via hash map; Micro-project: CSV cleaner (CLI).

Singly, doubly, circular lists; fast/slow pointers; in-place reversals.

Lab: Reverse in k-groups, detect cycle; Quiz 1.

Stack ADT & applications (parentheses, expression eval), Queue & Deque, circular queues.

Lab: Min-stack, sliding window maximum with deque; Micro-project: Undo/Redo engine.

Tree traversal (DFS: preorder/inorder/postorder), BFS (level order), BST ops.

Lab: Height, diameter, LCA (recursive & iterative).

AVL basics, heap (min/max), priority queue use-cases.

Lab: k-largest/merge k-sorted lists; Micro-project: Task scheduler using heap.

Adjacency list/matrix; DFS/BFS; connected components; topological sort.

Lab: Course scheduling (DAG), cycle detection; Quiz 2.

Dijkstra, BFS on unweighted graphs, Bellman-Ford (overview); MST (Kruskal/Prim).

Lab: City bus route finder (shortest path), network clustering (MST).

Linear vs Binary Search; boundary finding; custom comparators.

Bubble/Selection/Insertion (for insight), Merge/Quick/Heap Sort (for practice).

Lab: Sort visualizer; Micro-project: Log sorter for SMEs.

Activity selection, interval scheduling, Huffman coding idea.

Divide-and-Conquer patterns; Master Theorem (intuition).

Lab: Meeting rooms, min platform problem; closest pair (outline).

Overlapping subproblems, memoization vs tabulation; 1-D, 2-D DP.

Lab: Knapsack, coin change, LIS; Quiz 3.

Collision handling, custom hash; sets/maps; amortized analysis.

Lab: UPI-like transaction deduplicator (hash set/map).

Pattern catalog: prefix sums, difference arrays, binary search on answer, bit tricks.

Lab: Allocate books/paint fence (binary search on answer), bitmask DP (intro).

Pick one domain problem; design → implement → test → profile → README.

Examples:

• Smart Ration Queue Manager (queues + heap)

• Local Delivery Route Optimizer (graphs + Dijkstra)

• Crop-Yield Planner (DP)

• Metro Shortest Path CLI for nearest stations (graphs)

• Keyword Deduper & Ranker for small businesses (hashing + heap)

Write clean, efficient code using core DSA patterns in Python or C++ (learner’s choice).

Choose the right data structure/algorithm for real problems and justify trade-offs.

Analyze time and space using Big-O and optimize solutions iteratively.

Build and ship micro-products (CLI tools, libraries, APIs) that can be monetized.

Solve interview-style problems with structured thinking and clarity.

Maintain a portfolio (GitHub) showcasing solved problems and capstone work.

Duration: 4 Months (16 Weeks)

Load: ~8–10 hrs/week (2 weekday evenings × 2 hrs + Sat/Sun lab 3–4 hrs)

Mode: Blended (in-person/online), bilingual support (English + Hindi/Marathi as needed)

Track Choice: Python or C++ from Week 1 (Java optional add-on)

Labs & Tools: VS Code, GCC/Clang or Python 3.x, Git & GitHub, unit testing, simple profiling

Assessment: Quizzes (20%), Labs (30%), Projects (40%), Participation (10%)

Prerequisites: Basic computer usage; prior coding is helpful but not mandatory

Flipped micro-lectures: short, sharp concept drops; class time = practice.

Problem-of-the-Day: warm-up puzzles to build rhythm.

Pair programming & code reviews: learn to reason, argue, and improve.

Pattern-first approach: master templates (two-pointers, sliding window, DP, etc.).

Weekly micro-project: apply concepts to small, useful tools.

Show & ship: push to GitHub every week, present in lightning demos.

Interview drills: timed practice + whiteboard walkthroughs.

Freelancing: Build utilities (data parsers, optimizers, CLI tools), charge per project/feature.

Product gigs: Contribute to startups/SMEs—faster code = lower cloud bills.

Teaching/tutoring: Run local DSA clubs, paid workshops for school/college students.

Competitive placements: Strong DSA is still the core filter in many Indian tech interviews.

Entrepreneurship: Package your algorithms as micro-SaaS or libraries (billing, routing, scheduling).

Variables, data types, I/O; functions; debugging; CLI basics; Git/GitHub.

Big-O intuition; benchmarking simple loops.

Lab: Build a “Time-It” utility to compare naive vs optimized loops.

Loops, conditionals, recursion vs iteration; call stack.

Pattern: Two-Pointers, Sliding Window (intro).

Lab: Subarray sums, longest unique substring (string/window practice).

One-D, Two-D arrays, matrix traversal; string manipulation; hashing basics.

Lab: Matrix spiral, frequency counter via hash map; Micro-project: CSV cleaner (CLI).

Singly, doubly, circular lists; fast/slow pointers; in-place reversals.

Lab: Reverse in k-groups, detect cycle; Quiz 1.

Stack ADT & applications (parentheses, expression eval), Queue & Deque, circular queues.

Lab: Min-stack, sliding window maximum with deque; Micro-project: Undo/Redo engine.

Tree traversal (DFS: preorder/inorder/postorder), BFS (level order), BST ops.

Lab: Height, diameter, LCA (recursive & iterative).

AVL basics, heap (min/max), priority queue use-cases.

Lab: k-largest/merge k-sorted lists; Micro-project: Task scheduler using heap.

Adjacency list/matrix; DFS/BFS; connected components; topological sort.

Lab: Course scheduling (DAG), cycle detection; Quiz 2.

Dijkstra, BFS on unweighted graphs, Bellman-Ford (overview); MST (Kruskal/Prim).

Lab: City bus route finder (shortest path), network clustering (MST).

Linear vs Binary Search; boundary finding; custom comparators.

Bubble/Selection/Insertion (for insight), Merge/Quick/Heap Sort (for practice).

Lab: Sort visualizer; Micro-project: Log sorter for SMEs.

Activity selection, interval scheduling, Huffman coding idea.

Divide-and-Conquer patterns; Master Theorem (intuition).

Lab: Meeting rooms, min platform problem; closest pair (outline).

Overlapping subproblems, memoization vs tabulation; 1-D, 2-D DP.

Lab: Knapsack, coin change, LIS; Quiz 3.

Collision handling, custom hash; sets/maps; amortized analysis.

Lab: UPI-like transaction deduplicator (hash set/map).

Pattern catalog: prefix sums, difference arrays, binary search on answer, bit tricks.

Lab: Allocate books/paint fence (binary search on answer), bitmask DP (intro).

Pick one domain problem; design → implement → test → profile → README.

Examples:

• Smart Ration Queue Manager (queues + heap)

• Local Delivery Route Optimizer (graphs + Dijkstra)

• Crop-Yield Planner (DP)

• Metro Shortest Path CLI for nearest stations (graphs)

• Keyword Deduper & Ranker for small businesses (hashing + heap)