Learning Roadmap

Structured learning paths to achieve your IT career goals

Choose Your Learning Path

Follow our carefully designed roadmaps to progress from beginner to expert

🌐 Web Developer

Become a Full-Stack Web Developer

Stage 1: Foundations
HTML, CSS, JavaScript fundamentals
3-4 weeks
Stage 2: Frontend
React, state management, responsive design
4-5 weeks
Stage 3: Backend
Node.js, Express, RESTful APIs
3-4 weeks
Stage 4: Databases
SQL, NoSQL, data modeling
2-3 weeks
Stage 5: Capstone
Build and deploy a full-stack project
2-3 weeks
Total Duration: 12-16 weeks

📊 Data Scientist

Master Data Analysis & Machine Learning

Stage 1: Python Basics
Python programming for data analysis
2-3 weeks
Stage 2: Data Wrangling
Pandas, data cleaning, ETL pipelines
3-4 weeks
Stage 3: Statistics
Statistical analysis and hypothesis testing
3-4 weeks
Stage 4: ML Fundamentals
Machine learning algorithms and models
3-4 weeks
Stage 5: Capstone
End-to-end data science project
3-4 weeks
Total Duration: 14-18 weeks

☁️ Cloud Engineer

Master Cloud Platforms & DevOps

Stage 1: Cloud Basics
AWS, Azure fundamentals
2-3 weeks
Stage 2: Compute Services
EC2, Lambda, App Services
2-3 weeks
Stage 3: Containers
Docker, Kubernetes orchestration
3-4 weeks
Stage 4: CI/CD
Automation, pipelines, IaC
2-3 weeks
Stage 5: Capstone
Deploy and manage production infrastructure
2-3 weeks
Total Duration: 11-16 weeks

Detailed Learning Paths

🌐 Full-Stack Web Developer Path

This comprehensive path will transform you from a beginner to a job-ready full-stack web developer capable of building complete web applications.

Phase 1: Frontend Foundation (Weeks 1-8)

Build strong fundamentals in web technologies

  • HTML5 semantic markup and accessibility
  • CSS3 layouts, flexbox, grid, and animations
  • JavaScript ES6+ syntax and features
  • React fundamentals and hooks
  • State management (Redux/Context)
  • Testing React components

Phase 2: Backend Development (Weeks 9-12)

Learn server-side development with Node.js

  • Node.js runtime and NPM ecosystem
  • Express.js framework and middleware
  • RESTful API design and best practices
  • Authentication and authorization
  • Error handling and logging

Phase 3: Data & Databases (Weeks 13-14)

Master database design and management

  • Relational database design (SQL)
  • NoSQL databases (MongoDB)
  • Data modeling and normalization
  • Query optimization
  • ORM and database drivers

Phase 4: Deployment & DevOps (Week 15)

Deploy your applications to production

  • Version control with Git
  • Deployment platforms (Heroku, Vercel)
  • Environment variables and secrets
  • Monitoring and performance optimization

Phase 5: Capstone Project (Week 16)

Build a complete, production-ready project

  • Project planning and architecture
  • Full-stack implementation
  • Code review and testing
  • Deployment and documentation

📊 Data Science & Analytics Path

Transform into a data professional with expertise in analysis, visualization, and machine learning. Suitable for those with programming background or strong analytical skills.

Phase 1: Python Fundamentals (Weeks 1-3)

Strengthen Python programming skills for data science

  • Python syntax and data structures
  • Functions, modules, and packages
  • File I/O and working with data
  • NumPy for numerical computing

Phase 2: Data Manipulation & Exploration (Weeks 4-7)

Learn to work with real-world datasets

  • Pandas DataFrames and Series
  • Data cleaning and preprocessing
  • Exploratory data analysis (EDA)
  • Handling missing data and outliers

Phase 3: Data Visualization & Statistics (Weeks 8-11)

Visualize insights and conduct statistical analysis

  • Matplotlib and Seaborn visualization
  • Interactive visualizations
  • Descriptive statistics
  • Hypothesis testing and p-values
  • Regression analysis

Phase 4: Machine Learning (Weeks 12-15)

Build predictive models

  • Supervised learning algorithms
  • Unsupervised learning and clustering
  • Model evaluation and validation
  • Hyperparameter tuning
  • Ensemble methods

Phase 5: Capstone Project (Weeks 16-18)

Complete end-to-end data science project

  • Real-world dataset analysis
  • Model building and optimization
  • Results interpretation and presentation
  • Portfolio project documentation

☁️ Cloud Engineer & DevOps Path

Master cloud platforms and modern infrastructure management. Learn to architect, deploy, and maintain scalable cloud solutions.

Phase 1: Cloud Fundamentals (Weeks 1-3)

Understand cloud computing concepts

  • Cloud computing models and deployment
  • AWS core services overview
  • Azure fundamentals
  • Cloud architecture principles

Phase 2: Compute & Networking (Weeks 4-6)

Deploy and manage compute resources

  • EC2 instances and AMIs
  • Azure Virtual Machines
  • VPC and networking configuration
  • Load balancing and auto-scaling
  • Storage services (S3, Blob)

Phase 3: Containers & Orchestration (Weeks 7-9)

Containerize and orchestrate applications

  • Docker fundamentals and images
  • Docker Compose and networking
  • Kubernetes architecture
  • Deployment and services
  • ConfigMaps and secrets

Phase 4: Infrastructure as Code & CI/CD (Weeks 10-12)

Automate infrastructure management

  • Terraform basics and modules
  • CloudFormation templates
  • Jenkins and GitLab CI
  • Pipeline design and automation
  • Infrastructure monitoring

Phase 5: Capstone & Security (Weeks 13-16)

Deploy complete production infrastructure

  • Cloud security best practices
  • Identity and access management
  • Compliance and governance
  • Production deployment project