DSA Mastery Roadmap

From basic arrays to advanced graph algorithms β€” ~4 to 6 months at 2 hrs/day.

1

Foundation β€” Programming Basics

Duration: 2–3 weeks

Pick your language: C++ or Python

Learn syntax, loops, functions, and OOP basics.

Time & Space Complexity

Big-O notation, best/worst/average case analysis.

Recursion & Backtracking

Base case, call stack, factorial, fibonacci, permutations.

πŸ“Ή Video
CS50 by Harvard
~10 hrs
πŸ“– Book
Grokking Algorithms
Weekend read
πŸ‹οΈ Practice
LeetCode Easy Γ—20
2 weeks
2

Core Data Structures

Duration: 4–6 weeks

Arrays & Strings

Two pointers, sliding window, prefix sums.

Linked Lists, Stacks & Queues

Implementation from scratch, common patterns.

Hash Maps & Hash Sets

Frequency counting, anagrams, grouping problems.

Binary Search

Template-based approach, rotated arrays, search space reduction.

πŸ’‘ Tip: After each topic, do 5–10 LeetCode problems before moving on. Don't rush.
3

Trees, Heaps & Tries

Duration: 3–4 weeks

Binary Trees β€” DFS & BFS

Traversals, LCA, path sum, diameter.

Binary Search Trees

Insert, delete, validate, kth smallest.

Heaps / Priority Queues

Top-K problems, merge K lists, median of stream.

Tries

Word search, autocomplete.

4

Graphs & Advanced Algorithms

Duration: 4–5 weeks

Graph Traversal β€” DFS, BFS

Number of islands, connected components.

Shortest Path Algorithms

Dijkstra's, Bellman-Ford, Floyd-Warshall.

Topological Sort & Union Find

Course schedule, redundant connections.

5

Dynamic Programming

Duration: 4–5 weeks

1D DP β€” Fibonacci Patterns

Climbing stairs, house robber, coin change.

2D DP β€” Grid & String Patterns

LCS, edit distance, knapsack.

Interval & Bitmask DP

Advanced patterns for FAANG-level interviews.

Machine Learning Roadmap

From Python basics to deploying your first ML model β€” ~5 to 8 months.

1

Python & Math Foundation

Duration: 3–4 weeks

Python Essentials

Functions, OOP, list comprehensions, file I/O.

NumPy & Pandas

Array operations, DataFrames, data cleaning.

Linear Algebra & Statistics

Vectors, matrices, mean/variance, distributions.

2

Classical Machine Learning

Duration: 4–6 weeks

Supervised Learning

Linear Regression, Logistic Regression, Decision Trees, SVM.

Unsupervised Learning

K-Means, DBSCAN, PCA, Autoencoders.

Scikit-learn Workflows

Pipelines, cross-validation, hyperparameter tuning.

πŸ“Ή Course
Andrew Ng β€” ML Specialization
~50 hrs
πŸ‹οΈ Kaggle
Titanic & House Prices
2 projects
3

Deep Learning

Duration: 6–8 weeks

Neural Networks from Scratch

Forward/backward pass, gradient descent.

TensorFlow / PyTorch

Build and train CNNs for image classification.

NLP & Transformers (Intro)

Tokenization, embeddings, BERT basics.

4

Build & Deploy Projects

Duration: Ongoing

End-to-End ML Project

Data β†’ Model β†’ Evaluation β†’ Deployment on HuggingFace Spaces or Streamlit.

Kaggle Competitions

Join real competitions, read top notebooks, iterate.

🎯 Goal: Have 2–3 ML projects on GitHub before applying for internships.

Web Development Roadmap

From HTML basics to full-stack apps β€” ~4 to 6 months at your pace.

1

HTML & CSS Fundamentals

Duration: 2–3 weeks

HTML5 Semantics

Structure, forms, media, accessibility basics.

CSS β€” Flexbox & Grid

Layouts, responsive design, media queries.

Project: Portfolio Website

Build and deploy your personal site on GitHub Pages.

2

JavaScript & DOM

Duration: 4–5 weeks

JS Fundamentals

Variables, closures, promises, async/await, ES6+.

DOM Manipulation & Events

Build interactive UI without frameworks.

Fetch API & REST

Call public APIs, render data dynamically.

3

React.js & Modern Frontend

Duration: 4–6 weeks

React Fundamentals

Components, props, state, hooks (useState, useEffect).

Routing & State Management

React Router, Context API, intro to Redux.

4

Backend β€” Node.js & Databases

Duration: 5–6 weeks

Node.js & Express

Build REST APIs, middleware, authentication with JWT.

Databases β€” SQL & MongoDB

CRUD operations, relationships, aggregations.

Deploy a Full-Stack App

Vercel (frontend) + Railway or Render (backend).