Month 1: HTML, CSS, and Basic JavaScript (Frontend Foundations)

HTML:

  • Elements, tags, structure
  • Forms, tables, semantic HTML
  • Accessibility basics

CSS:

  • Selectors, box model, units
  • Flexbox, Grid
  • Transitions, animations
  • Responsive design (media queries)

JavaScript (Basics):

  • Variables, data types, operators
  • Conditionals and loops
  • Functions, scope, events
  • DOM manipulation
  • Basic form validation

Tools:

  • VSCode
  • Git & GitHub (basics)
  • Chrome DevTools

Projects:

  • Personal Portfolio Page
  • Responsive Landing Page
  • Basic JavaScript Game (e.g., Tic Tac Toe)

Month 2: Advanced JavaScript & Version Control

Advanced JS:

  • Arrays, objects, destructuring
  • ES6+ features (let/const, arrow functions, spread/rest)
  • Higher-order functions (map, filter, reduce)
  • Callbacks, Promises, async/await
  • Error handling
  • Local Storage

Version Control (Git):

  • Branching, merging
  • Pull requests, resolving conflicts
  • GitHub Collaboration

Projects:

  • To-Do App (with Local Storage)
  • Weather App using a public API

Month 3: Frontend Framework – React

React Basics:

  • Components, JSX, props & state
  • Event handling, forms
  • Conditional rendering, lists & keys

React Advanced:

  • useEffect, useState, hooks
  • Component lifecycle
  • React Router
  • Lifting state up & component hierarchy

Styling in React:

  • CSS Modules / Styled Components / TailwindCSS (choose one)

Projects:

  • Multi-page React Website
  • Expense Tracker or Notes App

Month 4: Backend Development – Node.js, Express.js, and Databases

Node.js + Express.js:

  • Node.js fundamentals
  • npm, package.json
  • Express.js routing, middleware
  • REST API basics
  • CRUD operations

Databases:

  • MongoDB & Mongoose (NoSQL)
  • Relational DB intro (PostgreSQL/MySQL – optional if time permits)

Authentication & Security:

  • JWT, bcrypt
  • Basic Auth flows

Projects:

  • RESTful API for blog or task manager
  • Simple Authentication System (login/signup with JWT)

Month 5: Connecting Frontend & Backend + Advanced Features

API Integration:

  • Connect React frontend with Express API
  • Axios/fetch, error handling
  • Token handling (JWT in headers)

Advanced Backend:

  • File uploads
  • Email sending
  • Role-based access control

Advanced React Concepts:

  • Context API
  • Performance optimization
  • Custom hooks

Deployment Tools:

  • GitHub Actions (basic CI/CD)
  • Vercel/Netlify for frontend
  • Render/Heroku for backend

Projects:

  • Full Stack App (e.g., Blog, Task Manager, E-commerce Lite)

Month 6: Final Project + Soft Skills + Interview Prep

Capstone Project:

  • Plan, design, build a full-stack project (solo or in a team)
  • Full CRUD, authentication, responsive design, deployment

Soft Skills:

  • Writing a tech resume & portfolio
  • GitHub best practices (clean commits, README files)
  • Communication & collaboration tools (Trello, Slack)

Interview Preparation:

  • Data Structures & Algorithms (basic)
  • JavaScript coding challenges (LeetCode/Easy to Medium)
  • System design basics
  • Behavioral questions

Bonus Topics (if time permits):

  • TypeScript
  • Next.js or Remix
  • WebSockets (real-time apps)
  • Docker basics

Data Science 6 Month

Day 1 To 14

  • Python basics: variables, data types, loops, conditionals
  • Functions, modules, error handling
  • Data structures: lists, dictionaries, sets, tuples
  • File handling (CSV, TXT)
  • Intro to object-oriented programming
  • Practice exercises and mini Python projects

Numpy & Pandas

Day 15-21

  • Numpy arrays, vectorized operations, indexing/slicing
  • Pandas: Series, DataFrames, importing/exporting data
  • Data cleaning: handling missing data, filtering, renaming, sorting
  • GroupBy, merging, concatenation
  • Real-world data manipulation exercises

Data Visualization

Day 22-28

  • Matplotlib: line plots, bar charts, scatter plots
  • Seaborn: histograms, boxplots, heatmaps, pairplots
  • Customizing plots and dashboards
  • Real-world data visualization project

Statistics & Probability

Day 29-35

  • Descriptive statistics: mean, median, mode, std, variance
  • Probability theory, combinations & permutations
  • Distributions: normal, binomial, Poisson
  • Hypothesis testing: t-test, chi-square
  • Correlation vs causation, p-values

Exploratory Data Analysis (EDA)

Day 36-42

  • EDA process: identifying trends, outliers, correlations
  • Feature engineering: creating new features, encoding, scaling
  • Handling skewness, distributions
  • EDA case study with a real dataset (e.g., Titanic, HR data)

Machine Learning Foundations

Day 43-49

  • ML concepts: supervised vs unsupervised, overfitting, bias-variance
  • Scikit-learn overview: train/test split, pipelines
  • Linear regression, logistic regression (theory + implementation)
  • KNN, Naive Bayes

Advanced Machine Learning

Day 50-56

  • Decision trees, Random Forests
  • Support Vector Machines (SVM)
  • K-Means clustering, DBSCAN
  • Model evaluation: accuracy, precision, recall, F1, ROC-AUC
  • Cross-validation and hyperparameter tuning

Projects & Deployment

Day 57-60

  • Mini Projects:
    • Classification (Titanic, Iris)
    • Regression (Housing prices)
    • Clustering (Customer segmentation)
  • Deployment:
    • Streamlit or Flask for web app
    • Deploy to Heroku or similar platform
  • Resume building, GitHub portfolio, mock interviews

Tools & Libraries

  • Languages: Python
  • Libraries: Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, Streamlit, Flask
  • Platforms: Jupyter Notebook, Google Colab, GitHub, Kaggle