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Course Syllabus · Data Structures

Data Structures Course Syllabus — Full Curriculum Breakdown

Complete Data Structures course syllabus breakdown — from arrays and linked lists to trees, graphs, and algorithms.

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Core DS → Advanced DS → Algorithms → Career Ready
Click a section to see the syllabus. Master data structures and algorithms to ace coding interviews.

Home / Tutorials / Course Syllabus / Data Structures Course Syllabus — Full Curriculum Breakdown

Course Syllabus · Data Structures

Data Structures Course Syllabus — Full Curriculum Breakdown

AK Ankit Kaushik · Lead Trainer, Algorithms & Data Structures | Updated 28 August 2026 | 18 min read Free · Syllabus Guide
CORE DS ADVANCED DS ALGORITHMS CAREER Section 1 Core Data Structures Arrays, Linked Lists, Stacks, Queues Foundation Section 2 Advanced Data Structures Trees, Graphs, Heaps, Hashing Build Section 3 Algorithms & Career Sorting, Searching, Interview Prep Launch Career Ready Job Ready Career Started Success
Data structures syllabus — Core DS, Advanced DS, Algorithms & Career.

Quick summary — Data Structures course syllabus

Data Structures and Algorithms (DSA) is the foundation of computer science. This guide provides a complete syllabus breakdown for a comprehensive Data Structures course — from core structures to advanced topics, algorithms, and career guidance.

In this guide you will learn:

  1. Core Data Structures — arrays, linked lists, stacks, queues.
  2. Advanced Data Structures — trees, graphs, heaps, hashing.
  3. Algorithms & Career — sorting, searching, and interview preparation.
  4. Career roadmaps for 6 backgrounds — B.Tech, BCA, Non-CS, Diploma, Freshers, Career Switchers.
  5. Interview Q&A — common DSA interview questions.

SECTION 01Core Data Structures

Core data structures form the foundation of DSA. Every programmer must master these:

Data Structure Key Concepts Time Complexity
Arrays Indexing, slicing, dynamic arrays (ArrayList) O(1) access, O(n) search
Linked Lists Singly, doubly, circular, operations O(n) access, O(1) insert/delete
Stacks LIFO, push, pop, peek, applications O(1) push/pop/peek
Queues FIFO, enqueue, dequeue, circular queue O(1) enqueue/dequeue 
Core Data Structures — Weekly Schedule:

Week 1: Arrays & Strings
- Day 1-2: Array basics, indexing, operations
- Day 3-4: Dynamic arrays (ArrayList, Vector)
- Day 5-7: String manipulation, problems

Week 2: Linked Lists
- Day 8-9: Singly linked list implementation
- Day 10-11: Doubly linked list, circular list
- Day 12-14: Linked list problems (reverse, detect cycle)

Week 3: Stacks
- Day 15-16: Stack implementation (array, linked list)
- Day 17-18: Stack applications (parentheses, expression evaluation)
- Day 19-21: Stack problems

Week 4: Queues
- Day 22-23: Queue implementation (array, linked list)
- Day 24-25: Circular queue, deque
- Day 26-28: Queue problems
core-ds-syllabus.md
Key insight: Master the core data structures before moving to advanced topics. They are the building blocks for everything in computer science.

SECTION 02Advanced Data Structures

Advanced data structures are essential for solving complex problems efficiently:

Data Structure Key Concepts Time Complexity
Trees Binary trees, BST, AVL, traversals O(log n) search (balanced)
Heaps Min-heap, Max-heap, priority queue O(log n) insert, O(1) peek
Hash Tables Hashing, collision resolution, HashMap O(1) average access
Graphs Representations, traversals (BFS, DFS) O(V+E) for traversals
Tries Prefix trees, autocomplete, search O(L) for search (L = length) 
Advanced Data Structures — Weekly Schedule:

Week 5: Trees
- Day 29-31: Binary trees, traversals (preorder, inorder, postorder)
- Day 32-34: Binary Search Trees (insert, delete, search)
- Day 35: AVL trees, rotations

Week 6: Heaps & Priority Queues
- Day 36-37: Min-heap, Max-heap implementation
- Day 38-39: Heap operations (heapify, extract)
- Day 40-42: Priority queue problems

Week 7: Hash Tables
- Day 43-45: Hash functions, collision resolution
- Day 46-47: HashMap implementation
- Day 48-49: Hashing problems

Week 8: Graphs
- Day 50-52: Graph representations (adjacency list, matrix)
- Day 53-54: BFS, DFS traversals
- Day 55-56: Graph problems
advanced-ds-syllabus.md
Key insight: Advanced data structures are crucial for optimizing code. Understanding when to use each structure is as important as knowing how they work.

SECTION 03Algorithms & Career

Mastering algorithms and interview preparation is the final step to becoming a successful programmer:

Topic Key Concepts Why It Matters
Sorting Algorithms Bubble, Quick, Merge, Heap Sort Foundation for optimization
Searching Algorithms Linear, Binary, DFS, BFS Essential for problem-solving
Dynamic Programming Memoization, tabulation, optimization Solve complex problems efficiently
Greedy Algorithms Selection, optimization problems Quick solutions for optimization
Interview Prep Mock interviews, coding challenges Ace technical interviews 
Algorithm Complexity Reference:

Sorting Algorithms:
- Bubble Sort: O(n²)
- Insertion Sort: O(n²)
- Merge Sort: O(n log n)
- Quick Sort: O(n log n) average
- Heap Sort: O(n log n)

Searching Algorithms:
- Linear Search: O(n)
- Binary Search: O(log n)
- DFS (Graph): O(V+E)
- BFS (Graph): O(V+E)

Dynamic Programming:
- Fibonacci: O(n) with DP
- Knapsack: O(n*W)
- Longest Common Subsequence: O(n*m)

Greedy Algorithms:
- Fractional Knapsack: O(n log n)
- Activity Selection: O(n log n)
algorithms-career.md
Key insight: Problem-solving ability is what separates great programmers from average ones. Practice consistently and learn patterns, not just solutions.

SECTION 04Career Roadmaps for Every Background

Here are 6 detailed career roadmaps tailored to your specific background — choose the one that fits you best. 

Career Roadmap: B.Tech / B.E. Graduates

Your Advantage: Strong engineering foundation + programming knowledge.
Your Challenge: Need to bridge theory with practical DSA skills.

DSA Learning Roadmap (Customized):
Week 1-4: Core DS (Arrays, Linked Lists, Stacks, Queues)
Week 5-8: Advanced DS (Trees, Graphs, Heaps, Hashing)
Week 9-10: Algorithms (Sorting, Searching, DP, Greedy)
Week 11-12: Interview prep and coding challenges

Key Skills to Highlight:
- Engineering problem-solving
- Strong DSA foundation
- Coding efficiency

Recommended Job Titles:
- Software Engineer
- Backend Developer
- Full Stack Developer
- Systems Engineer
career-roadmaps-dsa.md

SECTION 05Interview Q&A — Data Structures

Q1Why are data structures important?

Data structures are the foundation of efficient programming. They determine how data is stored, accessed, and manipulated — directly impacting performance and scalability.

Q2What's the difference between an array and a linked list?

Arrays have fixed size and offer O(1) random access. Linked lists are dynamic and allow O(1) insert/delete at known positions, but O(n) access time.

Q3What is a binary search tree (BST)?

A BST is a tree data structure where each node has at most two children. The left subtree contains values less than the node, and the right subtree contains values greater than the node.

Q4What is dynamic programming?

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems and storing the results to avoid recomputation.

Q5How can I prepare for DSA interviews?

Practice coding problems on platforms like LeetCode and HackerRank. Focus on understanding patterns, not just memorizing solutions. Mock interviews are also crucial.

SECTION 06Test yourself — Data Structures Quiz

Five questions. No sign-up.

0 / 5

Pick an answer to see why it is right or wrong.

SECTION 07Frequently asked questions

What's the most important data structure to learn first?

Arrays and linked lists are the most fundamental. Master these first, then move to stacks, queues, trees, and graphs.

How much time should I spend on DSA daily?

2-3 hours of focused practice daily is ideal. Consistency is more important than cramming — practice every day.

What if I don't have a degree in CS?

That's completely fine. Focus on building a strong DSA foundation and a portfolio of projects. Many successful developers are self-taught.

Can I combine DSA with other skills?

Yes — DSA is foundational for all programming roles. Combine it with web development, data science, or system design based on your career goals.

What if I'm not ready for a job after the course?

Use the extra time to practice more problems on LeetCode, build projects, or contribute to open source. Stay ready — opportunities come when you're prepared.

What I would do in the first 30 daysLayoff · 14 min Why sending 500 resumes won't workJob Search · 14 min Rebuild your resume for the AI job marketResume · 15 min Which skills to learn before applyingSkills · 14 min 90-day career recovery roadmapRecovery · 16 min How AI can help you prepare for interviewsAI · 11 min

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