Uncodemy Noida Campus DevOps Course Details Inside

Phase 1 — Daily Schedule (Weeks 1-4):
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review today's learning goals
9:00 AM — Deep study (2 hours)
   - Weeks 1-2: Linux filesystem, users, processes
   - Week 3: Networking basics
   - Week 4: Git and Bash scripting
12:00 PM — Lunch break
1:00 PM — Hands-on practice (2 hours)
   - Solve Linux exercises
   - Write small scripts
   - Push to GitHub
3:00 PM — Notes & consolidation (30 min)
4:00 PM — Read DevOps blogs (30 min)
6:00 PM — Exercise, rest
8:00 PM — Review mistakes, plan tomorrow
10:00 PM — Wind down

Commands you will practice daily:

# Navigate and manage files
ls, cd, pwd, cat, cp, mv, rm, find

# Permissions
chmod, chown, umask

# Processes
ps, top, kill, systemctl, journalctl

# Networking
curl, ping, netstat, ss, dig

# Git
git init, git clone, git status
git add, git commit, git push
git checkout -b, git merge

# Bash scripts
#!/bin/bash
for f in *.log; do
  echo "Processing $f"
done

FROM python:3.12-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 8000

CMD ["python", "app.py"]

version: "3.9"

services:
  app:
    build: .
    ports:
      - "8000:8000"
    environment:
      DB_HOST: db
      DB_USER: app
      DB_PASSWORD: app
    depends_on:
      - db

  db:
    image: postgres:16
    environment:
      POSTGRES_USER: app
      POSTGRES_PASSWORD: app
    volumes:
      - pgdata:/var/lib/postgresql/data

volumes:
  pgdata:

# Build and run
docker build -t myapp:1.0 .
docker run -p 8000:8000 myapp:1.0

# Compose
docker compose up -d
docker compose down

# Inspect
docker ps
docker logs 
docker exec -it  bash

# Push
docker tag myapp:1.0 myuser/myapp:1.0
docker push myuser/myapp:1.0

name: build-and-test

on:
  push:
    branches: [main]
  pull_request:

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.12"

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests
        run: pytest -v

      - name: Build Docker image
        run: docker build -t myapp:ci .

pipeline {
  agent any

  stages {
    stage('Checkout') {
      steps {
        git 'https://github.com/yourorg/myapp.git'
      }
    }

    stage('Install') {
      steps {
        sh 'pip install -r requirements.txt'
      }
    }

    stage('Test') {
      steps {
        sh 'pytest -v'
      }
    }

    stage('Build Image') {
      steps {
        sh 'docker build -t myapp:jenkins .'
      }
    }

    stage('Deploy') {
      steps {
        sh 'docker run -d -p 8000:8000 myapp:jenkins'
      }
    }
  }
}

apiVersion: apps/v1
kind: Deployment
metadata:
  name: myapp
spec:
  replicas: 3
  selector:
    matchLabels:
      app: myapp
  template:
    metadata:
      labels:
        app: myapp
    spec:
      containers:
        - name: myapp
          image: myuser/myapp:1.0
          ports:
            - containerPort: 8000
          env:
            - name: DB_HOST
              valueFrom:
                configMapKeyRef:
                  name: myapp-config
                  key: db_host

# Apply a manifest
kubectl apply -f deployment.yaml

# Check Pods
kubectl get pods
kubectl describe pod 

# Logs
kubectl logs 

# Port-forward to test locally
kubectl port-forward svc/myapp 8080:80

# Scale
kubectl scale deployment myapp --replicas=5

# Roll update
kubectl set image deployment/myapp myapp=myuser/myapp:1.1

Capstone project — Deploy the full stack:

Goal:
Deploy a small Flask or Node app to AWS EKS
with CI/CD on GitHub Actions.

Steps:
1. Containerize the app with Docker
2. Push image to Docker Hub or ECR
3. Write Kubernetes Deployment + Service
4. Create a small EKS cluster (or use kind locally)
5. Add GitHub Actions workflow:
   - Build image
   - Push image
   - kubectl apply
6. Add a README explaining:
   - Architecture diagram
   - How to deploy
   - What you would improve

This is what you show in interviews.

Batch options at Uncodemy Noida:

Weekday batch:
- Mon to Fri
- Evening slot (7:00 PM – 9:00 PM IST)
- Best for working professionals

Weekend batch:
- Saturday and Sunday
- Full-day sessions
- Best for students

Hybrid option:
- Attend in person or online
- Recordings available for missed sessions

Contact the campus for the next start date.

What is included in the course:

- 16 weeks of instructor-led training
- All tools installed and configured during class
- Two capstone projects
- Recording access
- Mentor doubt-clearing sessions
- Mock interviews
- Resume and portfolio review
- Placement assistance
- Certificate on completion

What is NOT included:
- AWS bills (use free tier carefully)
- Personal laptop required
- Job guarantee (no honest course offers one)

Before you join, ask:

1. Who is the lead trainer for my batch?
2. How many students per batch?
3. Are recordings available if I miss a class?
4. Are the projects done individually or in groups?
5. Do you help with AWS billing for the project?
6. How does placement support work exactly?
7. Are there instalment options?
8. Can I attend a free demo before paying?

These questions save you from surprises later.