HelloUniversity Icon HelloUniversity

Python-first learning path

Data Structures and Algorithms

Learn DSA through structured beginner-friendly lessons, Python examples, tracing activities, quick checks, applied projects, and VisualDSA demo links.

Start Lesson 1 View Roadmap Open VisualDSA
30
Lessons
5
Projects
Python
Primary language

Course Guide

Use the course as a practical problem-solving path

This course is designed for learners who want to understand why a solution works, how it grows, and when a different structure or algorithm would be a better choice.

What you will learn

  • Choose data structures based on how data is stored, accessed, searched, and updated.
  • Trace algorithms step by step before translating them into Python code.
  • Compare solutions using time and space complexity instead of guessing which one is better.
  • Apply DSA concepts in small projects that resemble real academic and software workflows.

Who this course is for

  • Beginner programming students who already know basic variables, conditions, loops, and functions.
  • IT and computer science learners preparing for coding activities, projects, or technical interviews.
  • Teachers who need a structured DSA path with lessons, practice tasks, quick checks, and projects.

How to study each lesson

  • Read the concept explanation first, then follow the trace tables or examples slowly.
  • Run or rewrite the Python examples so the operations become concrete.
  • Use the VisualDSA link as a guided practice layer, then finish the quick check or activity.

Learning Flow

Move from reading to practice

  1. 1 Read the lesson
  2. 2 Trace the example
  3. 3 Try the VisualDSA demo
  4. 4 Answer the quick check
  5. 5 Build a project

VisualDSA

Interactive demos are linked as practice companions.

VisualDSA pages are currently MVP placeholders. They reserve the official demo routes and connect each lesson or project to the future interactive layer.

Browse VisualDSA

Course Roadmap

Follow the lessons in official order

Start with foundations, move into core structures, then practice searching, sorting, design strategies, and applied projects.

Foundations

Build the thinking habits behind DSA: problem breakdown, complexity, recursion, and careful tracing.

Lesson 1 Introduction to DSA Understand what data structures and algorithms are, why they are studied together, and how they affect real programs. Lesson 2 Algorithmic Thinking Practice turning a problem into clear steps before writing code. Lesson 3 Time and Space Complexity Learn how to compare algorithms by how their time and memory use grow. Lesson 6 Recursion Trace functions that solve a problem by calling themselves with smaller inputs.

Linear Data Structures

Learn how arrays, strings, linked lists, stacks, and queues organize data in sequence.

Lesson 4 Arrays Use indexed collections to store, access, update, and traverse ordered data. Lesson 5 Strings Treat text as a sequence and solve common character-processing tasks. Lesson 7 Linked Lists Explore node-based sequences where each item points to the next item. Lesson 8 Stacks Use Last In, First Out behavior for undo, history, and function-call style problems. Lesson 9 Queues Use First In, First Out behavior for waiting lines, scheduling, and processing tasks.

Non-Linear Data Structures

Move into trees, heaps, and graphs where relationships branch, connect, and form networks.

Lesson 10 Trees Represent hierarchical data with roots, parents, children, and leaves. Lesson 11 Binary Search Trees Organize comparable values so searching and insertion follow left-right rules. Lesson 12 Tree Traversals Visit tree nodes in common orders such as preorder, inorder, postorder, and level order. Lesson 13 Heaps Learn priority-based tree structures used for efficient minimum or maximum access. Lesson 14 Graphs Model connected objects such as routes, friendships, networks, and dependencies. Lesson 15 Graph Traversals Trace breadth-first and depth-first ways to explore connected nodes.

Searching and Sorting

Compare core searching and sorting techniques through traces, tradeoffs, and Python examples.

Lesson 16 Linear Search Find a value by checking items one by one and understand when that is enough. Lesson 17 Binary Search Search sorted data by repeatedly cutting the search space in half. Lesson 18 Bubble Sort Trace a simple comparison sort and see why repeated passes can be expensive. Lesson 19 Selection Sort Sort by repeatedly selecting the next smallest or largest value. Lesson 20 Insertion Sort Build a sorted section one item at a time, like arranging cards in hand. Lesson 21 Merge Sort Use divide and conquer to split, sort, and merge data efficiently. Lesson 22 Quick Sort Partition data around a pivot and recursively sort each side.

Algorithm Design

Study common design strategies: brute force, divide and conquer, greedy choices, and dynamic programming.

Lesson 23 Brute Force Algorithms Start with the simplest correct approach and use it as a baseline for improvement. Lesson 24 Divide and Conquer Break large problems into smaller parts, solve them, and combine the results. Lesson 25 Greedy Algorithms Make locally best choices and learn when that strategy does or does not work. Lesson 26 Dynamic Programming Basics Reuse stored results to solve problems with overlapping subproblems.

Hashing and Advanced Structures

Use hash tables, sets, maps, and tries for faster lookup, grouping, and prefix-based tasks.

Lesson 27 Hash Tables Use keys and hashing to support fast lookup, insertion, and grouping. Lesson 28 Sets and Maps Apply uniqueness and key-value relationships to common programming tasks. Lesson 29 Tries Store strings by shared prefixes for autocomplete and word-search style problems.

Review

Connect the full course into practical problem-solving patterns and project decisions.

Lesson 30 DSA Review and Integration Review the full course and connect each structure or algorithm to practical decisions.

Applied Work

Build projects after core concepts

Each project asks you to choose and explain a structure, not only write code that runs.