Python Full Stack Developer Course in Pune with Placement

A targeted refresher rather than an introduction. Cover modern Python (f-strings, comprehensions, generators, type hints, dataclasses, walrus operator, match-case from 3.10), virtual environments with uv (the 2026 fast pip-replacement gaining adoption in Pune teams) or poetry, ruff for formatting + linting, mypy / pyright for type checking, and pytest as the universal default test runner. We assume basic Python fluency on day 1; this week levels the floor on the modern toolchain. By the end of week 1 every student has a clean development environment with uv, ruff, mypy, pytest, and Git all configured.

The mental model before the syntax. How a web application actually works — client and server, the request-response lifecycle, HTTP and HTTPS, three-tier architecture, and what separates a static site from a dynamic one. The toolchain is set up properly on day one (VS Code or PyCharm, the terminal, Git) rather than debugged in week six.

This week also fixes the vocabulary the rest of the course depends on: what an API is, what REST means, and what development, staging and production environments are for. It is short, and it prevents a specific kind of confusion that otherwise surfaces much later at a much worse time.

The four built-in structures, taught by what each is actually for rather than as a syntax tour. Lists and their mutation semantics, tuples and why immutability matters, sets and the operations that make them the right answer to deduplication and membership problems, and dictionaries — which underpin more Python code than any other structure.

Comprehensions get real time, because they are the difference between Python that reads like Python and Python that reads like translated Java. Functions then cover positional, keyword, default and variadic arguments, scope and the LEGB rule, lambdas, closures and decorators — the last being what makes Django and FastAPI's syntax legible rather than magical.

Classes, objects, inheritance, encapsulation and polymorphism as they are actually written in Python — including the parts that differ from Java and C# and trip up developers arriving from those languages: duck typing, dunder methods, properties instead of getters and setters, and multiple inheritance with the method resolution order.

Exception handling covers the full try, except, else and finally structure, exception hierarchies, custom exceptions and the discipline of catching narrowly. File handling covers text, CSV and JSON, context managers, and the `pathlib` module. Together these are the everyday competence that separates code that survives production from code that only works on the demo path.

The module that decides screening rounds. Pune product companies and services majors both run a coding filter before anyone reads your project work, and it tests exactly this material — arrays and strings, stacks and queues, linked lists, hash maps, trees, and the traversals over them.

Algorithms cover searching, the sorting family, recursion, two-pointer and sliding-window patterns, and an introduction to dynamic programming. Complexity analysis runs throughout rather than as an appendix, because the interview question is never just "does it work" — it is "what is the time complexity, and can you do better".

The frontend ground that Django templates render into and React components produce. Semantic HTML5, forms and input types, and accessibility attributes — written semantically, because a Django template built on div soup is as unreadable and unindexable as any other.

CSS covers selectors and specificity, the box model, Flexbox and Grid with a clear rule for choosing between them, and mobile-first responsive design. Tailwind then covers utility-first styling, configuration and component extraction, with Bootstrap noted as the alternative you will meet in existing Django projects and admin panels.

The second language, taught with the differences called out rather than glossed. Syntax, types and the coercion rules; functions, arrow functions and `this`; scope, hoisting and closures; and the array methods — map, filter, reduce — which look familiar to a Python developer and behave differently enough to matter.

The DOM and events come next, then asynchronous JavaScript: the event loop, promises, async/await and fetch. That last section is where Python developers most often stumble, because Python's async model and JavaScript's are genuinely different, and the differences are worth stating plainly rather than discovering through bugs.

Relational is the default in this course and PostgreSQL gets a full week of its own, but a Python full-stack developer meets MongoDB often enough that not covering it is a gap. Documents and collections, BSON types, CRUD and query operators, and the modelling decision that actually determines performance — embedding versus referencing.

Python integration uses PyMongo and Motor for async work, with Beanie as the ODM. Indexing and the aggregation pipeline get proper attention, and the module closes with a clear rule for when a document store is the right answer for a Python service and when reaching for one is a mistake you will pay for at reporting time.

Django from first principles, taught in the order that produces working applications fastest. Cover the project / app structure, Django ORM (models, fields, relationships, querysets, the discipline that prevents N+1 queries), database migrations, the URL dispatcher, function-based and class-based views, the Django template language, and the Django admin (which alone is a hiring differentiator — most institutes underuse it). Plus the discipline of Django settings management — environment variables, dotenv, settings split (base / dev / prod). By the end of week 3 every student has built a small CRM-style app with custom admin and full CRUD.

DRF is still the dominant Pune Django API toolkit. Cover serializers (the heart of DRF), ModelViewSets and the default router, authentication (Session, Token, JWT via djangorestframework-simplejwt), permissions and the discipline of permission classes, throttling, filtering / search / ordering, pagination, plus OpenAPI / Swagger generation via drf-spectacular. We compare with django-ninja (the Pydantic-based DRF alternative gaining adoption) and FastAPI — and we honestly say when each is the right tool.

The newer side of Python web. Django 5 async views and the async ORM, ASGI deployment, Django Channels for WebSocket. Then FastAPI as the standalone framework for high-performance APIs and AI-glue microservices — Pydantic v2, async endpoints, dependency injection, OpenAPI auto-generated. We build one async Django service and one FastAPI service so you internalise the ergonomic differences. Plus the production reality — when async earns its complexity (I/O-bound services with thousands of concurrent connections) and when sync Django is still the right tool.

Database depth that separates junior from mid-level Python engineers. Cover Postgres essentials at the level you will actually use — joins, transactions, indexes (B-tree / GIN / GiST / BRIN), EXPLAIN plans, materialised views, JSON / JSONB columns, full-text search, CTEs, window functions. Then the Django ORM mapping — raw SQL when you need it, F() and Q() expressions, select_related / prefetch_related (the #1 N+1 fix), aggregations, conditional updates. Plus connection pooling with pgbouncer (the production must-have most institutes skip).

The security baseline every production Python service needs. Cover Django's built-in auth (still the right default for most apps), JWT for SPA / mobile clients via simplejwt, social / OAuth login via django-allauth or python-social-auth (Google / GitHub / Azure AD), role-based and object-level permissions, rate limiting, and the security middleware stack (CSRF, XSS, SQL injection — Django defends most of these by default but you should understand how). Plus secrets management — django-environ, AWS Secrets Manager, Azure Key Vault — and the Pune-relevant compliance basics (PII handling, password storage, audit logging).

The backbone of every production Python service. Celery 5.x with Redis (the dominant Pune choice) — task queues, scheduled tasks via celery-beat, retry / backoff, error handling, monitoring with Flower. Cover the patterns that matter — idempotency, dead-letter queues, task chunking for large datasets, result backends, plus the discipline of designing tasks that survive worker restarts. We cover the alternatives (RQ for simpler stacks, dramatiq, Huey) and when each fits, plus a small section on django-q2 for teams that don't want Celery's complexity.

The frontend half of full-stack. React 19 essentials — components, props, state, hooks (useState, useEffect, useTransition, useOptimistic, useFormStatus), TypeScript-first throughout, Vite as the build tool, Tailwind for styling. Then the integration layer — TanStack Query for server state (the modern default), React Router 7 for routing, React Hook Form + Zod for forms, JWT interceptor pattern. We finish by wiring the React frontend to the Django + DRF API from week 4 — login, list / detail / create / update / delete, optimistic updates, error handling. (Optional Next.js path for students targeting SSR-heavy use cases.)

The week that turns a notebook into a service Pune teams will deploy. Docker multi-stage builds for Python apps (Gunicorn / uvicorn, the right WSGI / ASGI choice, the static-files story), Docker Compose for local-dev stacks (Django + Postgres + Redis + Celery worker + Celery beat). Then deployment — Render and Fly.io for fast deploys, AWS Elastic Beanstalk for the AWS path, Railway for the simplest path, plus a brief on AKS / EKS for larger teams. Observability — structured logging with structlog, Sentry for error tracking, OpenTelemetry instrumentation, Prometheus metrics via django-prometheus.

Two weeks of full-time capstone work plus structured interview preparation. Pick one of three capstone projects (see Capstone Projects). Mock interviews calibrated for Pune Python full-stack hiring panels — Amagi, Fyllo, Drip Capital, Innovaccer Pune, Persistent product engineering, plus Pune analytics product teams. Includes a coding round (Django / DRF / SQL live), a design round (decompose a feature, choose between sync and async, structure DRF serializers), and a behavioural round. Resume / LinkedIn / GitHub polish included.