Full-Stack Python Django + Next.js Training in Islamabad

🐍 Django + Next.js

Currently available in Islamabad

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Django is the most battle-tested web framework in Python — and in 2026 it powers Instagram, Pinterest, Disqus, Mozilla, Eventbrite, and thousands of SaaS products and enterprise applications worldwide. Its "batteries-included" philosophy means authentication, admin, ORM, migrations, caching, and security come built-in, letting teams move from idea to production faster than almost any other stack. Paired with Next.js 15 on the frontend, you get a clean separation between a powerful Python API backend and a modern, performant React frontend.

This course teaches Django as senior Python engineers use it in production — Django REST Framework for RESTful APIs, Strawberry for GraphQL, Celery for background tasks, Django Channels for real-time features, and a PostgreSQL data layer with carefully optimised queries. The Next.js frontend consumes the Django API with full TypeScript type safety, shared validation, and a polished deployment pipeline.

💡 Why Django + Next.js in 2026

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• Django's ORM, migrations, admin panel, and built-in security save weeks of setup time — the most productive Python backend framework for building full-featured applications
• Django REST Framework (DRF) remains the industry standard for building Python REST APIs — mature, well-documented, and supported by a vast ecosystem of extensions
• Python's dominance in data science and AI makes Django the natural choice when a web application needs to sit alongside ML models, data pipelines, or LLM integrations
• Next.js 15 App Router with React Server Components is the most capable frontend framework available — server-side rendering, streaming, and static generation in one cohesive system
• Strong remote demand: Django engineers with modern REST API and Next.js skills are highly sought after at international product companies and agencies
• The Python ecosystem in 2026 has expanded dramatically — FastAPI, Litestar, and Django Ninja offer compelling alternatives that this course covers as optional add-ons

📚 Module Breakdown

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Week 1 — Phase 0: Python 3.12+ & Django Foundations

This phase is not a Python introduction. It covers the modern Python language features that Django production code depends on, and Django's architecture from the ground up — the parts that most tutorial courses skip over.

1. Python 3.12+ features in production: type hints everywhere, TypedDict, Protocol, dataclasses, structural pattern matching, and the walrus operator
2. Type annotations in depth: generics with typing.Generic, Self type, ParamSpec, TypeVarTuple, and annotating complex data structures
3. Pydantic v2: the fastest Python data validation library — models, validators, computed fields, model serialisation, and integration with Django
4. Async Python: asyncio fundamentals, async/await, event loops, and understanding where Django's async support fits
5. Python packaging with uv: the modern, fast replacement for pip and virtualenv — dependency management in 2026
6. pyproject.toml: the standard project configuration file — replacing setup.py and requirements.txt
7. Django project structure: apps, settings split by environment, BASE_DIR, and the manage.py commands
8. Django settings best practices: django-environ for environment variables, separate dev/staging/prod settings, and SECRET_KEY management
9. Django's MVT architecture: models, views, templates, URL dispatcher, and the request/response cycle
10. Django apps: the modular app architecture — creating apps, app configs, and AppConfig.ready()
11. Django signals: pre_save, post_save, pre_delete, post_delete, and writing custom signals
12. Django management commands: writing custom management commands for maintenance tasks and data migrations
13. Testing with pytest-django: the pytest integration for Django — fixtures, the Django test client, and database transaction isolation
14. factory_boy: generating test data with factory classes — replacing manual fixture files
15. Ruff: the Rust-powered Python linter and formatter that replaces flake8, isort, and black in one tool
16. mypy with Django stubs: static type checking for Django projects — django-stubs and djangorestframework-stubs

Week 1–2 — Phase 1: Django ORM & Database Layer

Django's ORM is one of the most productive database interfaces in any web framework. This phase covers it in production depth — the patterns that keep queries fast, migrations safe, and data models clean at scale.

1. Model design: field types, field options, Meta class, verbose names, and model inheritance (abstract, proxy, multi-table)
2. Relationships: ForeignKey, OneToOneField, ManyToManyField — related_name, related_query_name, and the reverse manager
3. Custom model managers and querysets: encapsulating common query logic in reusable manager methods
4. QuerySet API in depth: filter, exclude, annotate, aggregate, values, values_list, select_related, prefetch_related, defer, only
5. The N+1 problem: identifying it with django-debug-toolbar and solving it with select_related and prefetch_related
6. F expressions and Q objects: database-level arithmetic, complex filter conditions, and avoiding race conditions
7. Annotations and aggregations: Count, Sum, Avg, Min, Max, and custom database expressions
8. Raw SQL in Django: Manager.raw(), connection.cursor(), and when raw SQL is the right answer
9. Django migrations: creating, squashing, and running migrations — data migrations with RunPython, and handling migration conflicts in teams
10. Database transactions: atomic(), select_for_update(), and handling deadlocks
11. Soft deletes: implementing deleted_at pattern with a custom manager
12. PostgreSQL-specific features with psycopg3: JSONField, ArrayField, HStoreField, and full-text search with SearchVector and SearchQuery
13. Django model constraints: UniqueConstraint, CheckConstraint, and database-level data integrity
14. Connection pooling with PgBouncer and django-db-geventpool: managing PostgreSQL connections for high-traffic Django applications
15. Database performance: EXPLAIN ANALYZE from Django, django-silk for query profiling, and index strategy

Week 2 — Phase 2: Django REST Framework — Building Production APIs

Django REST Framework is the gold standard for Python REST APIs. This phase covers DRF thoroughly — from serialisers and viewsets to authentication, permissions, and the advanced patterns that enterprise APIs require.

1. DRF architecture: APIView, GenericAPIView, mixins, and ViewSets — understanding the inheritance hierarchy
2. Serialisers: ModelSerializer, Serializer, nested serialisers, SerializerMethodField, and writable nested serialisers
3. Serialiser validation: field-level validation, object-level validation, and UniqueTogetherValidator
4. Routers: DefaultRouter, SimpleRouter, and custom route definitions with @action
5. Filtering: django-filter integration — FilterSet classes, search fields, and ordering fields
6. Pagination: PageNumberPagination, LimitOffsetPagination, CursorPagination — and customising page size
7. Authentication: SessionAuthentication, TokenAuthentication, and JWT with djangorestframework-simplejwt
8. JWT in depth: access tokens, refresh tokens, token rotation, token blacklisting, and custom claims
9. Permissions: IsAuthenticated, IsAdminUser, custom permission classes, and object-level permissions
10. Throttling: AnonRateThrottle, UserRateThrottle, and custom throttle policies
11. API versioning: URL path versioning, namespace versioning, and header versioning in DRF
12. Content negotiation: supporting multiple response formats from the same endpoint
13. Exception handling: custom exception handlers, consistent error response format, and mapping Django exceptions to HTTP status codes
14. drf-spectacular: generating OpenAPI 3 documentation from DRF views — schema customisation and Swagger UI
15. django-cors-headers: configuring CORS for the Next.js frontend client
16. Testing DRF APIs with pytest-django: APIClient, force_authenticate(), and testing every endpoint

Week 2–3 — Phase 3: GraphQL with Strawberry & Advanced API Patterns

GraphQL is increasingly used alongside REST in Python backends — particularly for data-heavy frontends that need flexible querying. Strawberry is the modern, type-annotation-first GraphQL library for Python, replacing the older Graphene library.

1. GraphQL fundamentals for backend engineers: schema definition, queries, mutations, subscriptions, and the N+1 problem
2. Strawberry: the Python-first GraphQL library using dataclasses and type annotations — no more string-based schemas
3. Strawberry types: @strawberry.type, @strawberry.input, @strawberry.enum, and @strawberry.interface
4. Resolvers: field resolvers, root value resolvers, and the info object for accessing request context
5. Django integration: strawberry-graphql-django for generating types and resolvers from Django models
6. DataLoader: the solution to the N+1 problem in GraphQL — batching and caching database lookups per request
7. Authentication in Strawberry: accessing the Django request and validating JWT tokens in GraphQL resolvers
8. Permissions in Strawberry: the @strawberry.permission_class decorator for field and type-level authorisation
9. Mutations: creating, updating, and deleting data through GraphQL — input validation and error handling
10. GraphQL subscriptions with Django Channels: real-time data over WebSockets from a Strawberry subscription
11. File uploads in GraphQL: the multipart request spec and handling file uploads in Strawberry
12. Relay-style pagination: cursor-based pagination with the Connection type — the standard for paginating large datasets in GraphQL
13. Testing Strawberry GraphQL: using the Strawberry test client for unit testing resolvers and integration testing queries
14. REST vs GraphQL decision framework: when to expose REST endpoints, when GraphQL adds value, and running both from the same Django project

Week 3 — Phase 4: Authentication, Authorisation & Security

Production Django applications require multi-layered security. This phase covers the full authentication and authorisation stack — from social login to fine-grained object permissions to security hardening.

1. Django authentication system: the User model, AbstractUser vs AbstractBaseUser, custom user models, and the authentication backend
2. django-allauth: the comprehensive authentication package — email/password, social OAuth (Google, GitHub, Apple), email verification, and password reset
3. djangorestframework-simplejwt: JWT authentication for DRF — access/refresh token flow, token claims, and blacklisting
4. Social authentication for APIs: connecting OAuth providers to a DRF API consumed by Next.js
5. Role-based access control: django-guardian for per-object permissions, and building a custom RBAC system with Django groups
6. Row-level security: filtering querysets based on the authenticated user — the ownership pattern in Django
7. Multi-factor authentication: TOTP-based 2FA with django-otp and QR code generation
8. API key authentication: generating and managing long-lived API keys for programmatic access
9. Django security checklist: DEBUG=False enforcement, ALLOWED_HOSTS, HTTPS settings, CSRF, clickjacking protection, and security middleware
10. SQL injection prevention: Django ORM parameterisation and safe raw query patterns
11. django-axes: login attempt throttling and brute force protection
12. Content Security Policy: django-csp for setting CSP headers on API responses

Week 3–4 — Phase 5: Next.js 15 Frontend

The Next.js frontend consumes the Django REST Framework and GraphQL APIs — with TypeScript type safety, auto-generated API clients, and the full App Router architecture.

Next.js 15 App Router:

1. App Router architecture: server components, client components, layouts, loading states, and error boundaries
2. TypeScript API client generation from the drf-spectacular OpenAPI spec using openapi-typescript — shared types between Django and Next.js
3. Data fetching in server components: async/await directly in components, fetch with cache options, and revalidation
4. TanStack Query for client-side data: fetching, caching, background refetch, and optimistic updates against the Django API
5. Apollo Client for GraphQL: connecting to the Strawberry Django API from Next.js — queries, mutations, and subscriptions
6. Authentication with NextAuth.js v5: credentials provider connecting to Django JWT, session management, and protected routes
7. Next.js Middleware: edge authentication guards and redirecting unauthenticated users
8. Server Actions for mutations: calling Django API endpoints from Server Actions — progressive enhancement
9. Form handling: React Hook Form with Zod validation, sharing validation logic with Pydantic schemas where possible

UI and advanced Next.js:

1. Tailwind CSS v4: utility-first styling, the new CSS-based configuration, and component variants with cva()
2. shadcn/ui: accessible, unstyled component library built on Radix UI — the standard Next.js component system
3. TanStack Table: server-side sorted, filtered, and paginated data tables backed by DRF's pagination and filtering
4. Real-time with Django Channels: consuming Django WebSocket connections from Next.js client components
5. File uploads: drag-and-drop UI with progress tracking connecting to the Django file upload endpoint
6. Internationalisation with next-intl: multi-language Next.js applications backed by Django's translation framework
7. Metadata API: dynamic SEO from server components — Open Graph, Twitter cards, and JSON-LD structured data
8. Playwright E2E testing: full browser tests covering the complete Django + Next.js stack

Week 4–5 — Phase 6: Celery, Channels, Caching & Production Readiness

The final phase covers the production infrastructure that takes a Django application from working locally to running reliably under real traffic — asynchronous task processing, real-time features, caching strategy, observability, and deployment.

Celery — asynchronous task processing:

1. Celery architecture: workers, brokers (Redis, RabbitMQ), result backends, and the task lifecycle
2. Defining tasks: @shared_task, @app.task, task options (retry, max_retries, countdown, eta)
3. Task chaining: chain(), group(), chord(), and canvas primitives for complex task workflows
4. Periodic tasks with Celery Beat: cron-style scheduled tasks and dynamic schedule management with django-celery-beat
5. Celery Flower: the real-time monitoring dashboard for Celery workers and tasks
6. Error handling in Celery: automatic retries, exponential backoff, dead-letter handling, and task failure notifications
7. Task priority queues: routing tasks to dedicated workers for SLA-sensitive workloads

Django Channels — real-time features:

1. Django Channels architecture: ASGI server (Daphne / Uvicorn), channel layers, consumers, and routing
2. WebSocket consumers: AsyncWebsocketConsumer — connect, receive, send, and disconnect lifecycle
3. Channel layers with Redis: broadcasting messages to groups of connected clients
4. Authentication in Channels: authenticating WebSocket connections using JWT tokens in the query string or headers
5. Real-time notifications: pushing database changes to connected Next.js clients via WebSockets
6. GraphQL subscriptions over WebSockets: Strawberry subscriptions with Django Channels transport
7. Presence tracking: tracking which users are online with channel groups and Redis

Caching:

1. Django cache framework: the cache API, cache backends (Redis, Memcached, database), and cache key versioning
2. django-redis: the Redis cache backend for Django — cache configuration, connection pooling, and serialisation options
3. Per-view caching: @cache_page decorator and cache_control headers
4. Low-level cache API: cache.get(), cache.set(), cache.delete(), cache.get_many() for granular caching
5. Cache invalidation patterns: cache.delete_pattern(), cache tagging with django-cache-memoize, and event-driven invalidation
6. DRF response caching: caching serialised API responses with cache decorators and conditional request support

Observability and performance:

1. Structured logging with structlog: JSON log output, bound loggers, processors, and context variables
2. Django Debug Toolbar: local development profiling — SQL queries, cache hits, template rendering times
3. django-silk: profiling request/response cycles and SQL queries in staging environments
4. OpenTelemetry Python SDK: automatic instrumentation for Django, DRF, Celery, and SQLAlchemy — exporting to Jaeger or Tempo
5. Sentry for Django: error tracking, performance monitoring, and release tracking
6. Health check endpoints: django-health-check for database, cache, Celery worker, and storage health — Kubernetes probe integration

Deployment:

1. ASGI deployment: running Django with Uvicorn + Gunicorn for both HTTP and WebSocket support
2. Docker for Django: multi-stage Dockerfile — separate images for web, Celery worker, and Celery Beat scheduler
3. Docker Compose: full local stack — Django, Next.js, PostgreSQL, Redis, Celery, Flower, and Nginx
4. Static files and media: WhiteNoise for serving static files, and S3-compatible storage for user-uploaded media
5. Django deployment checklist: collectstatic, ALLOWED_HOSTS, SECRET_KEY rotation, database connection pooling, and HTTPS enforcement
6. GitHub Actions CI/CD: Ruff, mypy, pytest, Next.js build, Docker push, and deployment pipeline

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🔌 Optional Add-On Modules

(2 weeks each — additional fee per module. Can be taken individually or combined.)

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Add-On 1: AWS Deployment Track

Deploy Django and Next.js across the AWS ecosystem — containerised ECS deployments, managed databases, S3 storage, SQS queues, and full CI/CD automation.

1. ECS Fargate: containerised Django on managed container infrastructure — separate task definitions for web (Uvicorn), Celery worker, and Celery Beat
2. AWS RDS (PostgreSQL): managed database — connection configuration with psycopg3, read replicas, and automated backups
3. ElastiCache (Redis): managed Redis for Celery broker, Django cache, and Django Channels layer
4. Amazon SQS: Celery broker configuration for SQS — task routing, visibility timeout, and dead-letter queues
5. AWS S3 with django-storages: replacing local media storage — the DEFAULT_FILE_STORAGE and STATICFILES_STORAGE settings, presigned URLs
6. AWS SES: Django email backend for transactional email via SES
7. AWS Lambda for Django: Mangum adapter for running Django ASGI on Lambda — use cases and limitations
8. Next.js on AWS: OpenNext adapter for Lambda + CloudFront — full App Router support
9. Application Load Balancer: load balancing across multiple Django containers with health check endpoints
10. CloudFront: CDN for Next.js assets and Django static files — cache headers and invalidation strategy
11. AWS Secrets Manager: injecting Django SECRET_KEY and database credentials into ECS task environment
12. Infrastructure as Code with AWS CDK (Python): provisioning the entire stack in Python — the most natural IaC language for Django teams
13. GitHub Actions: run Ruff, mypy, pytest, push Docker images to ECR, deploy new task definitions to ECS
14. CloudWatch: structlog JSON log ingestion, custom metrics, and alarms for Celery queue depth and error rates

Add-On 2: Google Cloud Deployment Track

Deploy Django and Next.js on Google Cloud — Cloud Run for the web layer, Cloud SQL for managed PostgreSQL, and the full GCP data ecosystem.

1. Cloud Run with Django: containerised Uvicorn + Django on Cloud Run — handling stateless execution and WebSocket limitations
2. Cloud Run Jobs: running Celery workers and management commands as Cloud Run Jobs
3. Cloud SQL (PostgreSQL): managed PostgreSQL with Cloud SQL Auth Proxy for secure connections
4. Memorystore (Redis): managed Redis for Celery and Django cache
5. Cloud Storage with django-storages: GCS backend for Django media and static files
6. Cloud Tasks: Django Celery alternative for serverless task execution — pushing tasks to Cloud Tasks HTTP targets
7. Cloud Pub/Sub: event-driven messaging for Django microservices on GCP
8. Secret Manager: loading Django settings from GCP Secret Manager via the google-cloud-secret-manager client
9. Next.js on Cloud Run: containerised Next.js server deployment
10. Terraform on GCP: provisioning Cloud Run services, Cloud SQL, Memorystore, and networking
11. Cloud Build: CI/CD — Ruff, pytest, Docker build, push to Artifact Registry, deploy to Cloud Run
12. Cloud Monitoring: OpenTelemetry exporter for GCP, custom metrics, and uptime checks for Django health endpoints

Add-On 3: Azure Deployment Track

Deploy Django on Microsoft Azure — for teams working with enterprise clients standardised on Azure or building applications within the Microsoft ecosystem.

1. Azure Container Apps: serverless Django containers — separate apps for web, Celery worker, and scheduler
2. Azure Database for PostgreSQL: managed PostgreSQL with flexible server and connection pooling
3. Azure Cache for Redis: managed Redis for Celery and Django Channels layer
4. Azure Service Bus: Celery broker for Azure — queues and topics with Django Celery integration
5. Azure Blob Storage with django-storages: the Azure storage backend for Django media files
6. Azure Container Registry (ACR): storing Django and Next.js Docker images
7. Azure Key Vault: secrets management for Django — DATABASE_URL, SECRET_KEY, and API keys
8. Azure Static Web Apps: deploying Next.js with the Azure Next.js runtime
9. Infrastructure with Bicep: provisioning Container Apps, databases, Redis, and storage
10. Azure DevOps Pipelines: CI/CD for Django + Next.js — lint, type check, test, build, and deploy
11. Azure Monitor and Application Insights: OpenTelemetry integration, distributed tracing, and performance dashboards

Add-On 4: AI Integration Track

Django's position in the Python ecosystem makes it the natural home for AI-powered web applications. This add-on integrates LLM APIs, RAG pipelines, and vector search directly into Django — using Celery for background AI processing and the Django ORM for storing AI artefacts.

1. LLM APIs from Django: OpenAI, Anthropic, and Google Gemini using the official Python SDKs from Django views and Celery tasks
2. Streaming LLM responses from Django to Next.js: Django StreamingHttpResponse with SSE — token-by-token streaming from a DRF endpoint
3. Structured output from LLMs: parsing model responses into Pydantic v2 models with validation
4. Celery-based AI processing: offloading LLM API calls to background workers — non-blocking AI features for web requests
5. Embeddings in Django: generating embeddings via OpenAI or sentence-transformers and storing in PostgreSQL
6. pgvector with Django: django-pgvector for storing and querying vector embeddings with the Django ORM
7. Building a RAG pipeline in Django: document ingestion as Celery tasks, chunking, embedding, pgvector storage, and retrieval in DRF views
8. LangChain with Django: using LangChain's Django-compatible components for orchestrating complex AI workflows
9. LlamaIndex with Django: document indexing and query engines backed by Django models and PostgreSQL
10. AI-powered Django Admin: adding LLM-powered features to the Django admin — content generation, summarisation, and semantic search
11. Rate limiting and cost control: per-user token budgets tracked in the Django ORM, Redis-based rate limits on AI endpoints
12. Caching LLM responses: Django cache backend for storing and retrieving LLM completions for identical requests
13. Testing AI features: mocking LLM API calls in pytest-django tests — deterministic, fast test suites without real API calls

Add-On 5: FastAPI Alternative Track

FastAPI is the fastest-growing Python web framework in 2026 — and for many use cases it is now the better choice over Django REST Framework. This add-on teaches FastAPI as a production backend framework in its own right, covering when to migrate from Django to FastAPI and when to run both side by side.

1. FastAPI architecture: ASGI-native, dependency injection, automatic OpenAPI generation, and the Starlette foundation
2. Path operations: GET, POST, PUT, PATCH, DELETE — route parameters, query parameters, request bodies, and response models
3. Pydantic v2 integration: request validation, response serialisation, and computed fields — FastAPI's native validation layer
4. Dependency injection: FastAPI's powerful DI system — declaring dependencies, sub-dependencies, and scoped dependencies (per-request database sessions)
5. SQLAlchemy 2.0 async with FastAPI: the async ORM session, declarative models, and Alembic for migrations — the production alternative to Django ORM
6. Alembic migrations: versioned schema management — autogenerate, upgrade, and downgrade commands
7. Authentication in FastAPI: OAuth2 password bearer, JWT with python-jose or PyJWT, and the security dependency pattern
8. Background tasks: FastAPI's built-in BackgroundTasks for simple async work — and when to reach for Celery instead
9. WebSockets in FastAPI: the native WebSocket endpoint — real-time features without Django Channels
10. Server-Sent Events: streaming responses for real-time LLM output and progress updates
11. Testing FastAPI: the TestClient and AsyncClient — testing with a real database via testcontainers
12. Django vs FastAPI decision framework: startup time, team size, admin requirements, async needs, and performance benchmarks
13. Running FastAPI alongside Django: separate microservices — Django for the admin/CMS, FastAPI for the high-performance API layer
14. Migrating a DRF endpoint to FastAPI: a practical migration walkthrough with shared Pydantic models

Add-On 6: Modern Python API Frameworks — Litestar & Django Ninja Track

Two other Python API frameworks have gained significant traction in 2025–2026: Django Ninja for teams that want FastAPI-style type safety without leaving Django, and Litestar (formerly Starlite) as a high-performance ASGI framework that competes with and often outperforms FastAPI. This add-on covers both.

Django Ninja (Week 1 of this add-on):

1. Django Ninja architecture: FastAPI-style decorators built on top of Django — keeping the Django ORM and admin, gaining type-safe APIs
2. Router and API class: defining endpoints with @api.get, @api.post, @api.put, @api.delete
3. Schema classes: Pydantic v2-based request and response schemas — direct replacement for DRF serialisers
4. Path, query, and body parameters: auto-parsed and validated by Pydantic v2
5. Django Ninja authentication: JWT, API key, and session-based auth as injected dependencies
6. Async views in Django Ninja: writing async endpoint handlers that use Django's async ORM support
7. File uploads in Django Ninja: the UploadedFile type and handling multipart requests
8. Pagination in Django Ninja: built-in paginator classes and cursor pagination
9. Auto-generated OpenAPI: Swagger UI and Redoc from Django Ninja's schema with zero configuration
10. Testing Django Ninja: the TestClient and async test patterns with pytest-django
11. Django Ninja vs DRF: when to migrate, when to run both, and when DRF's maturity wins

Litestar (Week 2 of this add-on):

1. Litestar architecture: a batteries-included ASGI framework with a clean, opinionated design — built for high performance and developer experience
2. Route handlers: @get, @post, @put, @patch, @delete decorators with full type inference
3. Dependency injection: Litestar's powerful DI system — Provide, dependencies scope, and testing injection
4. DTOs (Data Transfer Objects): Litestar's first-class DTO system — DataclassDTO, MsgspecDTO, and PydanticDTO for request/response transformation
5. SQLAlchemy plugin: first-class SQLAlchemy 2.0 integration — session management, repository pattern, and async support
6. Authentication and authorisation: the AbstractAuthenticationMiddleware and guard system
7. WebSockets and SSE: native async WebSocket handlers and Server-Sent Events in Litestar
8. Caching: the built-in response cache with configurable backends
9. OpenAPI: auto-generated, fully configurable OpenAPI 3.1 documentation
10. Litestar vs FastAPI: performance benchmarks, DX comparison, and the cases where Litestar wins
11. When to choose Litestar over FastAPI or Django: the decision framework for 2026

📅 Schedule & Timings

Choose one group only based on your availability. Max 5 candidates per group to ensure individual attention and hands-on support.

Weekday Groups:

• Group 1: Mon–Wed, 10 AM – 1 PM
• Group 2: Mon–Wed, 4 PM – 7 PM

Weekend Groups:

• Group 3: Sat & Sun, 10 AM – 2 PM
• Group 4: Sat & Sun, 4 PM – 8 PM

📍 Location: In-house training in Islamabad
📱 Online option may be arranged for out-of-city participants

🛠️ Core Tools & Technologies

• Backend: Python 3.12+, Django 5.x, Django REST Framework, Strawberry GraphQL
• Database: PostgreSQL (psycopg3), Django ORM, django-pgvector, Redis (django-redis)
• Auth: djangorestframework-simplejwt, django-allauth, django-guardian
• Async / Real-time: Django Channels, Celery, Celery Beat, Redis channel layer, Daphne / Uvicorn
• API Docs: drf-spectacular (OpenAPI 3), Swagger UI
• Frontend: Next.js 15 App Router, React 19, TypeScript, Tailwind CSS v4, shadcn/ui, TanStack Query, Apollo Client, React Hook Form, NextAuth.js v5
• Testing: pytest-django, factory_boy, Playwright, mypy + django-stubs
• Code Quality: Ruff, mypy, pre-commit
• Observability: structlog, OpenTelemetry Python SDK, Sentry, django-silk
• DevOps: Docker (multi-stage), Docker Compose, GitHub Actions, uv

✅ Prerequisites

• Comfortable writing Python (functions, classes, decorators, list comprehensions)
• Basic understanding of HTTP, REST APIs, and JSON
• Familiar with SQL and relational database concepts
• Basic JavaScript / TypeScript knowledge helpful for the Next.js section
• No prior Django or Next.js experience required

🎯 Who This Is For

• Python developers transitioning into full-stack web development
• Data engineers and ML engineers who want to expose models via a web API and frontend
• Developers moving from Flask or older Django versions to modern Django 5.x + DRF
• Engineers targeting remote Python backend or full-stack roles
• Developers building data-driven SaaS products with a Python backend

💳 Course Fee & Booking

• ⌛ Core Program Duration: 5 Weeks
• ➕ Each Add-On Module: 2 additional weeks (additional fee per module)
• 📦 Available Add-Ons: AWS Deployment · Google Cloud · Azure · AI Integration · FastAPI Alternative · Litestar & Django Ninja
• 🔒 Seats: 5 only per group

👉 Click here to book via WhatsApp