Build a solid tech base for Data Analytics, Full Stack, or DevOps. This phase is your launchpad!

Weeks 1–3: Basics & Core Concepts

• Variables, Data Types, Operators, Conditionals, Loops
• Functions, Lists, Tuples, Sets, Dictionaries

Weeks 4–5: Intermediate Python

• File & Exception Handling, Modules, Comprehensions, Lambda, OOP

Week 6: Extras

• Virtual Environments, PEP 8

Tools: VS Code / Jupyter Notebook

Weeks 1–3: Basics

• Syntax, Variables, Control Structures, Arrays, Strings, OOP

Weeks 4–5: Intermediate

• Exception Handling, File I/O, Collections, Interfaces, Abstract Classes

Week 6: Practical

• Mini Projects: Banking System / Library App

Tools: IntelliJ IDEA / Eclipse

Weeks 1–2: Basics

• Syntax, Data Types, Operators, Conditionals, Loops, Functions

Week 3: Intermediate

• Pointers, Arrays, Strings, Structures, Unions

Week 4: Advanced

• Dynamic Memory, File Handling, Recursion, Bitwise Ops

Tools: Turbo C / GCC / Code::Blocks

• Code Editor: VS Code
• Live Preview: Live Server Extension
• Browser: Chrome / Firefox (DevTools)
• Version Control: Git + GitHub

• Basic Tags, Lists, Tables, Forms, Semantic Tags
• Mini Projects: Bio Page, Contact Form, Resume (HTML only)

• Selectors, Box Model, Flexbox, Grid, Responsive Design
• Mini Projects: Styled Resume, Pricing Card, Grid Gallery, Landing Page

• Variables, Data Types, Operators, Control Structures, Functions, Arrays, Objects, Events
• Practice: Calculator, To-Do (console), Quiz App

• DOM Manipulation, Event Listeners, Form Validation, ES6+ Features
• Projects: Form Validation, DOM To-Do, Quiz App, Light/Dark Toggle

• Portfolio, Blog, E-Commerce Page, Weather App, Deploy via GitHub Pages

🗓️ 8-Week Timeline

💡 Mini Project Ideas

• Portfolio Website (HTML/CSS/JS)
• CLI To-Do App (Python/Java/C)
• GitHub Workflow Practice

Master web development foundations for Data Analytics, Full Stack, or DevOps.

• Java Syntax, OOP, Collections, Exceptions, File Handling, Multithreading, Java 8, Git Basics

• HTML5, CSS3, JavaScript (ES6+), TypeScript, React/Angular, Responsive Design, Git Advanced

• Spring Boot, Spring MVC, Spring Security, Hibernate ORM, JPA + DB, Testing, Maven/Gradle

• SQL, DB Design, Indexing, Transactions, NoSQL (MongoDB)

• CI/CD, Docker, Docker Compose, Kubernetes, AWS, Monitoring

• Agile, Jira, Trello, Code Reviews, Docs, Communication

• Capstone Projects, Deployment, Portfolio Website

• Phase 1: JavaScript Core (ES6+, DOM, Async)
• Phase 2: React.js (Components, Hooks, Router)
• Phase 3: Angular (CLI, Components, Data Binding, Routing)
• Phase 4: Redux (Store, Toolkit) [For React State Management]
• Phase 5: TypeScript (Used in Angular)
• Phase 6: Node.js + Express.js (APIs, Middleware)
• Phase 7: MongoDB (CRUD, Mongoose, Aggregation)
• Phase 8: JWT Authentication (Both Angular & React Auth Flows)
• Phase 9: REST API Integration (Axios/HttpClient, State Management)
• Phase 10: Deployment (MongoDB Atlas, Render, Vercel, Netlify, CI/CD)

• Phase 0: Prerequisites (HTML, CSS, PHP, SQL, Git)
• Phase 1: Laravel Setup (Composer, Artisan, .env)
• Phase 2: Core Laravel (MVC, Routing, Blade)
• Phase 3: CRUD & DB (Migrations, Eloquent, Forms)
• Phase 4: Auth & Middleware (Breeze, Middleware)
• Phase 5: Advanced Laravel (API, Jobs, Events, Deployment)
• Phase 6: Final Project (Full-stack App)

Master the tools that deal with massive-scale data and make your analytics career future-proof.

• SELECT, WHERE, GROUP BY, ORDER BY
• Joins: INNER, LEFT, RIGHT, FULL
• Aggregations & CASE, COALESCE
• Subqueries & Common Table Expressions (CTEs)
• Window Functions: ROW_NUMBER(), RANK(), LEAD()
• Views, Indexing, and Performance Optimization

• Power Query for data cleaning and ETL
• Data Modeling & Relationships
• DAX Basics: CALCULATE, FILTER, SUMX
• Time Intelligence: DATESYTD, SAMEPERIODLASTYEAR
• Creating Dashboards, Filters, and Slicers
• Row-Level Security (RLS)
• Publishing to Power BI Service & Auto Refresh

• Virtual Warehouses, Databases, Schemas
• SQL Queries in Snowflake
• Time Travel & Zero-Copy Cloning
• Data Sharing, Warehousing Best Practices
• Performance & Cost Optimization Techniques

• Hive Architecture: Metastore, Driver, Compiler
• HiveQL: SQL-like query language
• Data Partitions & Bucketing
• Using Joins, Built-in & Custom UDFs
• Working with Big Data in Hadoop Ecosystem

• Core Concepts: RDDs, DataFrames
• Transformations vs Actions
• Spark SQL for Querying Big Data
• Machine Learning with Spark MLlib
• Real-Time Data Processing using Spark Streaming

• Kafka Architecture: Topics, Brokers, Producers, Consumers
• Partitioning, Replication & Offsets
• Building Real-Time Data Pipelines
• Kafka Connect & Kafka Streams APIs

• Using Notebooks and Clusters
• Delta Lake for Storage & Version Control
• Unified Analytics: SQL + Python + Spark
• MLflow for Model Tracking and Experimentation
• Streamlined Data Engineering on the Cloud

• Columnar Storage and Data Warehousing Concepts
• Writing & Tuning SQL Queries
• Redshift Spectrum for Querying External Data
• Federated Queries and Integration with BI Tools
• Performance Tuning and Cost Optimization

📌 What You'll Learn

• Virtualization & Cloud Computing
  • Definition of virtualization, hypervisors, and practical use cases
  • Cloud models: IaaS, PaaS, SaaS
  • Benefits: scalability, flexibility, cost-efficiency
• AWS Overview
  • What is AWS?
  • Global infrastructure and service categories

• AWS IAM || AWS CLI
  • Users, groups, roles, policies || Installing and configuring CLI for automation

• EC2 (Elastic Compute Cloud)
  • Launching instances, key pairs, security groups
• EBS (Elastic Block Store)
  • Attaching volumes, creating snapshots
• AMI (Amazon Machine Image)
  • Creating reusable machine images
• S3 (Simple Storage Service)
  • Buckets, object storage, lifecycle policies
  • Static website hosting and versioning

• ELB (Elastic Load Balancer)
  • Distribute traffic across instances
• ASG (Auto Scaling Groups)
  • Scale up/down based on demand

• RDS (Relational Database Service)
  • MySQL, PostgreSQL, backups, scaling
• DynamoDB
  • NoSQL tables, indexing, high-speed access
• AWS Lambda
  • Serverless function triggers & automation

• CloudFormation
  • Writing templates to automate AWS setup
  • Managing stack deployments
• Terraform (Bonus)
  • Open-source IAC alternative

• VPC (Virtual Private Cloud)
  • Subnets, routing tables, Internet Gateways
• Route 53
  • Domain registration and DNS routing
• CloudFront
  • CDN for global content caching and delivery

• AWS Billing & Organization
  • Budget tracking and consolidated accounts
• Amplify (Full Stack Demo)
  • Deploy frontend/backend apps
  • CI/CD for full-stack workflows

• ECS (Elastic Container Service)
  • Docker container management
• EKS (Elastic Kubernetes Service)
  • Managed Kubernetes cluster setup

• SSL/TLS Certificates
  • HTTPS setup and encryption handling
• DNS Practical Session
  • Understand DNS with real-world scenarios

🚀 Microsoft Azure Certification Path – 2025

• AZ-900: Microsoft Azure Fundamentals
• AI-900: Microsoft AI Fundamentals
• DP-900: Microsoft Azure Data Fundamentals

• AZ-104: Microsoft Azure Administrator Associate
• AZ-204: Microsoft Azure Developer Associate
• DP-100: Microsoft Azure Data Scientist Associate
• AI-102: Microsoft Certified: Azure AI Engineer Associate
• DP-203: Microsoft Azure Data Engineer Associate
• DP-300: Microsoft Azure Database Administrator Associate
• PL-300: Microsoft Data Analyst Associate
• AZ-500: Microsoft Azure Security Engineer Associate

• AZ-305: Microsoft Azure Solutions Architect Expert
• AZ-400: Microsoft DevOps Engineer Expert

• Programming Language: Start with Python or Go.
• Operating Systems: Learn OS concepts (focus on Linux).
• Networking Basics: Understand HTTP, HTTPS, TCP/IP, DNS, SSH.

• Git: Learn Git commands, branching, merging, rebasing.
• Platforms: GitHub, GitLab, Bitbucket.

• AWS: IAM, EC2, S3, CloudWatch.
• Azure: Azure DevOps, VMs, Blob Storage.
• GCP: Compute Engine, Cloud Storage, Stackdriver.

• Docker: Dockerfiles, managing images, running containers.
• Docker Compose: Multi-container applications.

• Kubernetes: Pods, Deployments, Services, ConfigMaps.
• Terraform: Infrastructure as code.
• Ansible: Configuration management and automation.

• Tools: GitHub Actions, GitLab CI/CD, Jenkins.
• Practices: Automated testing, integration, and deployment.

• Monitoring: Prometheus.
• Visualization: Grafana.
• Logging: ELK Stack (Elasticsearch, Logstash, Kibana).

• DevSecOps: Integrate security into pipelines.
• Tools: SonarQube (code analysis), Vault (secrets management).

• MLOps: Machine learning workflows in DevOps.
• SRE (Site Reliability Engineering): Scalability, reliability, incident management.

• What is System Design?
• HLD vs LLD
• Functional vs Non-functional Requirements

• Load Balancer, CDN, API Gateway
• Caching (Redis), Queues (Kafka)
• SQL vs NoSQL, Sharding, CAP Theorem

• Horizontal vs Vertical Scaling
• Rate Limiting, Auto-scaling
• Metrics: TPS, SLA, RPS

• SOLID Principles
• Factory, Singleton, Strategy Patterns
• Authentication & Authorization (JWT, OAuth)

• HTTPS, JWT, OAuth
• Monitoring (Prometheus, Grafana)
• Logging (ELK Stack)

• Design: YouTube, Instagram, WhatsApp, Netflix
• Design: URL Shortener, Notification System, Rate Limiter

🚦 Ready to build your skills? Stick to this roadmap and level up your tech career!