Technology & Software Development

Data Structures & Algorithms

Arrays • Trees • Graphs • Dynamic Programming • Complexity

Develop strong problem-solving and programming fundamentals through data structures, algorithms, complexity analysis, and coding problems.

⏱ 3 months (self-paced) 📊 Intermediate 🗣 English & Hindi 🏆 Certificate included

Course overview

Data Structures & Algorithms builds the problem-solving foundation that every strong programmer needs.

You will learn how to analyse the time and space complexity of your code, implement the core data structures from scratch, and apply classic algorithmic techniques — recursion, sorting, searching, greedy methods and dynamic programming.

Each topic is followed by topic-wise coding problems of increasing difficulty, so you learn to recognise patterns and approach new problems with confidence.

Course at a glance

Duration3 months (self-paced)
Difficulty levelIntermediate
FormatOnline · self-paced
LanguageEnglish & Hindi

What you will learn

  • Analyse time and space complexity using Big-O notation
  • Solve problems with arrays, strings and hashing
  • Think recursively and apply backtracking
  • Implement and compare searching and sorting algorithms
  • Build linked lists, stacks and queues from scratch
  • Work with trees, binary search trees and heaps
  • Represent graphs and apply BFS, DFS and shortest-path algorithms
  • Apply greedy techniques and dynamic programming
  • Recognise common problem-solving patterns
  • Write efficient, well-tested solutions

Course curriculum

12 modules · 53 lessons
Module 1: Complexity Analysis4 lessons
📖 Why efficiency matters 🔒
📖 Big-O, Big-Omega and Big-Theta 🔒
📖 Analysing loops and recursive functions 🔒
📖 Space complexity 🔒
Module 2: Arrays & Strings5 lessons
📖 Array operations and traversal 🔒
📖 Two-pointer technique 🔒
📖 Sliding window technique 🔒
📖 Prefix sums 🔒
📖 String manipulation problems 🔒
Module 3: Recursion & Backtracking4 lessons
📖 Thinking recursively 🔒
📖 Recursion tree and base cases 🔒
📖 Backtracking: permutations and subsets 🔒
📖 N-Queens and maze problems 🔒
Module 4: Searching & Sorting5 lessons
📖 Linear and binary search 🔒
📖 Binary search on answers 🔒
📖 Bubble, selection and insertion sort 🔒
📖 Merge sort and quick sort 🔒
📖 Counting sort and choosing the right sort 🔒
Module 5: Linked Lists4 lessons
📖 Singly linked lists 🔒
📖 Doubly and circular linked lists 🔒
📖 Fast and slow pointer problems 🔒
📖 Reversal and merging problems 🔒
Module 6: Stacks & Queues4 lessons
📖 Stack implementation and applications 🔒
📖 Queue, circular queue and deque 🔒
📖 Monotonic stack problems 🔒
📖 Expression evaluation 🔒
Module 7: Hashing4 lessons
📖 Hash tables and collision handling 🔒
📖 Hash maps and hash sets in practice 🔒
📖 Frequency counting problems 🔒
📖 Designing an LRU cache 🔒
Module 8: Trees & Binary Search Trees4 lessons
📖 Binary tree traversals 🔒
📖 Height, diameter and views of a tree 🔒
📖 Binary search tree operations 🔒
📖 Lowest common ancestor and path problems 🔒
Module 9: Heaps & Priority Queues4 lessons
📖 Binary heap implementation 🔒
📖 Heap sort 🔒
📖 Top-K and K-way merge problems 🔒
📖 Median of a data stream 🔒
Module 10: Graphs6 lessons
📖 Graph representation 🔒
📖 Breadth-first and depth-first search 🔒
📖 Topological sort 🔒
📖 Shortest paths: Dijkstra's algorithm 🔒
📖 Minimum spanning trees 🔒
📖 Union-Find (disjoint set) 🔒
Module 11: Greedy Algorithms & Dynamic Programming5 lessons
📖 Greedy strategy and proofs 🔒
📖 Introduction to dynamic programming 🔒
📖 Memoisation and tabulation 🔒
📖 Knapsack and subsequence problems 🔒
📖 DP on grids and strings 🔒
Module 12: Problem-Solving Strategy4 lessons
📖 A step-by-step approach to new problems 🔒
📖 Recognising common patterns 🔒
📖 Testing and edge cases 🔒
📖 Mixed practice sets and revision 🔒

Practical projects

Custom Data Structures Library

Implement linked lists, stacks, queues, heaps and a hash map from scratch with tests.

LRU Cache

Design and build an LRU cache using a hash map and a doubly linked list.

Pathfinding Visualiser

Apply BFS and Dijkstra's algorithm to find routes on a grid or map.

Topic-wise Problem Sets

Graded coding problems for every topic, from easy to challenging.

Tools & software

Java or PythonVisual Studio Code / IntelliJ IDEAOnline coding practice platformsGit & GitHub

Who is this course for?

  • Students preparing for technical interviews and coding assessments
  • Graduates strengthening their programming fundamentals
  • Working developers who want to write more efficient code
  • Learners who have completed a programming course and want to go deeper

Career applications

The skills from this course can be applied to roles and projects such as:

  • Preparation for technical interviews and coding tests
  • Software Developer and Software Engineer roles
  • Writing efficient code for large data and performance-critical systems
  • Competitive programming

Requirements

  • Working knowledge of one programming language (Java or Python recommended)
  • Comfort with loops, functions and basic data types
  • Our Python Programming or Java Full Stack course covers these prerequisites

Certificate

🏆

Certificate of Completion

On successfully completing the course requirements — lessons, practical projects/assignments and assessments — you receive a Vector Tech Academy Certificate of Completion. Every certificate carries a unique certificate ID and a QR code that anyone (a client, an employer or an institute) can verify on our website. The certificate recognises that you completed this course and its projects; it is not a university degree or a government-recognised qualification.

Certificate terms →

Frequently asked questions

Which programming language is used for DSA?
Concepts are explained independently of language, with implementations in Java and Python. You can practise in either.
Is this course suitable for complete beginners?
You should know the basics of one programming language first. If you are new to coding, start with Python Programming.
Does this course help with technical interviews?
Yes. It covers the data structures, algorithms and problem-solving patterns commonly used in technical interviews and coding assessments.

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