AICPE India provides skill development courses, AICPE franchise opportunities, authorised training centre support, certificate verification, computer courses, vocational courses, accounting courses, digital marketing courses, typing courses, job-oriented courses and self-employment courses across India.

CERTIFICATE IN DATA STRUCTURES &AMP; ALGORITHMS

AICPE skill-based course designed for practical learning, career development, job-readiness and self-employment opportunities.

Duration: 4 MONTHS Course Code: C1555 Skill Development Course AICPE Certification Support

Enquire Now Find Centre Verify Certificate

Course Highlights

Practical skill-based learning
Useful for career growth
Suitable for students and job seekers
Helpful for self-employment awareness
Certificate verification support where records are available

Job-Oriented Courses Self-Employment

Course Syllabus

Course Name: Data Structures & Algorithms

Course Duration: 4 Months

Course Objectives

By the end of 4 months, learners will be able to:

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.

Course Overview

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

Teaching Methodology

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.

Why This Matters

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).

Detailed Syllabus

Month 1 — Foundations that Stick

Week 1: Programming Basics & Mindset

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.

Week 2: Control Flow & Core Patterns

Loops, conditionals, recursion vs iteration; call stack.
Pattern: Two-Pointers, Sliding Window (intro).
Lab: Subarray sums, longest unique substring (string/window practice).

Week 3: Data Structures 1 — Arrays & Strings

One-D, Two-D arrays, matrix traversal; string manipulation; hashing basics.
Lab: Matrix spiral, frequency counter via hash map; Micro-project: CSV cleaner (CLI).

Week 4: Data Structures 2 — Linked Lists & Complexity

Singly, doubly, circular lists; fast/slow pointers; in-place reversals.
Lab: Reverse in k-groups, detect cycle; Quiz 1.

Month 2 — Classic Structures & Searching

Week 5: Stacks & Queues

Stack ADT & applications (parentheses, expression eval), Queue & Deque, circular queues.
Lab: Min-stack, sliding window maximum with deque; Micro-project: Undo/Redo engine.

Week 6: Trees 1 — Binary Trees & BSTs

Tree traversal (DFS: preorder/inorder/postorder), BFS (level order), BST ops.
Lab: Height, diameter, LCA (recursive & iterative).

Week 7: Trees 2 — Balanced Trees & Heaps

AVL basics, heap (min/max), priority queue use-cases.
Lab: k-largest/merge k-sorted lists; Micro-project: Task scheduler using heap.

Week 8: Graphs 1 — Representations & Traversals

Adjacency list/matrix; DFS/BFS; connected components; topological sort.
Lab: Course scheduling (DAG), cycle detection; Quiz 2.

Month 3 — Algorithms that Pay Off

Week 9: Graphs 2 — Shortest Paths & MST

Dijkstra, BFS on unweighted graphs, Bellman-Ford (overview); MST (Kruskal/Prim).
Lab: City bus route finder (shortest path), network clustering (MST).

Week 10: Searching & Sorting Deep Dive

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.

Week 11: Greedy & Divide-and-Conquer

Activity selection, interval scheduling, Huffman coding idea.
Divide-and-Conquer patterns; Master Theorem (intuition).
Lab: Meeting rooms, min platform problem; closest pair (outline).

Week 12: Recursion & Dynamic Programming

Overlapping subproblems, memoization vs tabulation; 1-D, 2-D DP.
Lab: Knapsack, coin change, LIS; Quiz 3.

Month 4 — From Patterns to Products

Week 13: Advanced Hashing & Practical Optimizations

Collision handling, custom hash; sets/maps; amortized analysis.
Lab: UPI-like transaction deduplicator (hash set/map).

Week 14: Problem Solving Playbook

Pattern catalog: prefix sums, difference arrays, binary search on answer, bit tricks.
Lab: Allocate books/paint fence (binary search on answer), bitmask DP (intro).

Week 15: Capstone Build Sprint

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)

Note: The course contents are flexible and can be modified based on the specific requirements of learners or location, ensuring relevance and customization to meet the needs of Indian students interested in self-employment and entrepreneurship in Artificial Intelligence.

Course Name: Data Structures & Algorithms

Course Duration: 4 Months

Course Objectives

By the end of 4 months, learners will be able to:

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.

Course Overview

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

Teaching Methodology

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.

Why This Matters

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).

Detailed Syllabus

Month 1 — Foundations that Stick

Week 1: Programming Basics & Mindset

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.

Week 2: Control Flow & Core Patterns

Loops, conditionals, recursion vs iteration; call stack.
Pattern: Two-Pointers, Sliding Window (intro).
Lab: Subarray sums, longest unique substring (string/window practice).

Week 3: Data Structures 1 — Arrays & Strings

One-D, Two-D arrays, matrix traversal; string manipulation; hashing basics.
Lab: Matrix spiral, frequency counter via hash map; Micro-project: CSV cleaner (CLI).

Week 4: Data Structures 2 — Linked Lists & Complexity

Singly, doubly, circular lists; fast/slow pointers; in-place reversals.
Lab: Reverse in k-groups, detect cycle; Quiz 1.

Month 2 — Classic Structures & Searching

Week 5: Stacks & Queues

Stack ADT & applications (parentheses, expression eval), Queue & Deque, circular queues.
Lab: Min-stack, sliding window maximum with deque; Micro-project: Undo/Redo engine.

Week 6: Trees 1 — Binary Trees & BSTs

Tree traversal (DFS: preorder/inorder/postorder), BFS (level order), BST ops.
Lab: Height, diameter, LCA (recursive & iterative).

Week 7: Trees 2 — Balanced Trees & Heaps

AVL basics, heap (min/max), priority queue use-cases.
Lab: k-largest/merge k-sorted lists; Micro-project: Task scheduler using heap.

Week 8: Graphs 1 — Representations & Traversals

Adjacency list/matrix; DFS/BFS; connected components; topological sort.
Lab: Course scheduling (DAG), cycle detection; Quiz 2.

Month 3 — Algorithms that Pay Off

Week 9: Graphs 2 — Shortest Paths & MST

Dijkstra, BFS on unweighted graphs, Bellman-Ford (overview); MST (Kruskal/Prim).
Lab: City bus route finder (shortest path), network clustering (MST).

Week 10: Searching & Sorting Deep Dive

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.

Week 11: Greedy & Divide-and-Conquer

Activity selection, interval scheduling, Huffman coding idea.
Divide-and-Conquer patterns; Master Theorem (intuition).
Lab: Meeting rooms, min platform problem; closest pair (outline).

Week 12: Recursion & Dynamic Programming

Overlapping subproblems, memoization vs tabulation; 1-D, 2-D DP.
Lab: Knapsack, coin change, LIS; Quiz 3.

Month 4 — From Patterns to Products

Week 13: Advanced Hashing & Practical Optimizations

Collision handling, custom hash; sets/maps; amortized analysis.
Lab: UPI-like transaction deduplicator (hash set/map).

Week 14: Problem Solving Playbook

Pattern catalog: prefix sums, difference arrays, binary search on answer, bit tricks.
Lab: Allocate books/paint fence (binary search on answer), bitmask DP (intro).

Week 15: Capstone Build Sprint

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)

Read Full Syllabus

Eligibility

SSC or 10th

Important: AICPE is an autonomous private skill development and certification organisation. Acceptance or eligibility may vary depending on employer, institution, department or purpose.

Skill Courses After 10th After 12th

Explore AICPE Skill Development Courses

AICPE offers practical skill development courses through authorised training centres across India. Students can explore job-oriented courses, self-employment courses, computer courses, accounting courses, digital marketing courses, typing courses, vocational courses and soft skills programs.