Python Course in Pune with Placement

Set up Python 3.13 with virtual environments and pip, write your first script, then move into the language properly — variables, primitive types, control flow, functions, and the rich literal syntax (f-strings, list comprehensions). By the end of this module you'll be comfortable reading any Python codebase and writing 100-line scripts without lookup. We use modern tooling — uv or poetry for environments, ruff for formatting, mypy for type hints — so you absorb professional habits from day one.

Python's string handling in depth, because a very large share of real Python work is reading text, reshaping it and writing it back out. Slicing, the method set, f-strings and the format mini-language, and encoding — which is where beginners meet their first genuinely confusing production bug, usually involving a UTF-8 file opened as ASCII.

Regular expressions follow: pattern syntax, groups, greedy against lazy matching, and the `re` module's compile, search, findall and sub. The module closes on the discipline of knowing when a regex is the wrong tool, and you build a log-parsing utility that extracts structured records from unstructured text.

Lists, tuples, sets, dictionaries — when each is the right choice and how each is implemented (CPython internals at a useful level). Comprehensions for declarative data transformation, generator expressions for lazy evaluation, and the collections module for specialised structures (defaultdict, Counter, deque). We finish with a small project that uses all four core structures in their idiomatic forms.

Classes, instances, inheritance, polymorphism, and Python's distinctive features — properties, dunder methods, dataclasses (the modern way to write 80% of classes), and Protocol typing. Includes the discipline that hiring panels test for — when to use a class vs a function, when to use composition over inheritance, and how to design for testability.

Read and write text and binary files using context managers, parse and emit JSON, handle CSV data with the standard library, walk directory trees with pathlib, and build small scripts that rename files, validate folders, clean text and generate reports. This is Core Python as it appears in everyday work: automation, data handling, logs, configuration files and repeatable utilities, without jumping into pandas or web frameworks too early.

The modules that separate Python that reads like Python from Python that reads like translated Java. `collections` gives you `defaultdict`, `Counter`, `deque`, `namedtuple` and `OrderedDict` — each of which replaces a five-line pattern beginners write by hand every time.

`itertools` covers lazy iteration, chaining, grouping, combinations and infinite sequences; `functools` covers `lru_cache`, `partial`, `reduce` and `wraps`. The framing throughout is the same: before writing a loop, check whether the standard library already did it — faster, tested, and in one line that a reviewer will recognise immediately.

Dates are where correct-looking programs quietly produce wrong answers, so they get their own treatment. `datetime`, `date`, `time` and `timedelta`; naive against aware datetimes and why mixing them raises; time zones with `zoneinfo`; parsing and formatting with `strptime` and `strftime`; and ISO 8601 as the format to use whenever you control the choice.

The module then covers the practical data work that surrounds dates in real scripts — validating input, handling missing and malformed values, normalising inconsistent formats, and the defensive habits that stop one bad row from ending a batch job halfway through.

Learn how Python projects are organised once they grow past a single file. Cover modules, packages, imports, __name__ == '__main__', virtual environments, dependency installation, requirements files, pyproject.toml awareness, command-line arguments and clean folder layout. We also introduce GitHub workflow, README writing and code-review habits so your Core Python projects look professional rather than like classroom fragments.

What professional Python looks like in 2026. Type hints are not enforced at runtime and are still expected in any serious codebase: annotating functions, variables and collections, `Optional` and unions, generics, and running `mypy` so the hints actually catch something rather than decorating the file.

Dataclasses remove the boilerplate of `__init__`, `__repr__` and `__eq__`, and `frozen=True` gives immutability for free. Enums, `pathlib` instead of string paths, and the walrus operator and structural pattern matching close the module — the features that make current Python read very differently from Python written five years ago.

Strengthen the Python features that separate copy-paste beginners from confident programmers: iterators, generators, decorators, context managers, regular expressions, logging, configuration, datetime handling and type hints. These topics appear constantly in automation scripts, backend code, testing tools and data pipelines. Each concept is tied to a practical mini-task so it becomes usable rather than theoretical.

Use Core Python to connect with the outside world. Learn SQLite and basic relational-database access, parameterised queries, simple CRUD operations, HTTP requests to public APIs, response parsing, retry awareness and automation scripts that move data between files, APIs and databases. This module gives the practical bridge into backend, data and automation tracks while staying within Core Python.

The difference between a script and a tool someone else can run. Logging replaces `print` — levels, handlers, formatters, writing to file and console, and structured logging that can actually be searched. The rule taught is simple and rarely followed: `print` is for a human watching now, logging is for a human investigating later.

Command-line interfaces with `argparse` cover arguments, options, defaults, subcommands and help text. Configuration covers environment variables, `.env` files, config precedence, and keeping secrets out of source control — the mistake that ends up in a public repository often enough to be worth a section of its own.

Doing several things at once in a language with a Global Interpreter Lock, which is the subject most Python developers are vaguest about and interviewers most enjoy probing. The GIL is explained plainly, along with its actual consequence: threads help with I/O-bound work and do not help with CPU-bound work.

From there: `threading` and its locks, `multiprocessing` for true parallelism, `concurrent.futures` as the interface worth reaching for first, and `asyncio` — the event loop, coroutines, `async`/`await` and `gather`. The judgement being taught is which to choose, and the honest answer that for most scripts the correct choice is none of them.

The habits that make your work reviewable and your portfolio credible. Git as collaboration rather than three memorised commands: branching, merging, resolving conflicts, pull requests, review etiquette, and a commit history a stranger can follow.

Clean Python covers PEP 8 and what it is actually for, project layout, docstrings worth writing, `ruff` and `black` so formatting stops being a discussion, and the readability habits that get code approved rather than returned. You finish with a public repository — README, requirements, a working entry point — because that repository is what a Pune hiring panel opens before your CV.

Turn Core Python knowledge into reliable code. Learn debugging in VS Code or PyCharm, pytest basics, fixtures, assertions, mocking awareness, coverage, and code-review habits. Then practise DSA patterns in Python: arrays/lists, strings, dictionaries, sets, stacks, queues, recursion, sorting and searching. This module prepares students for both the capstone and entry-level technical interviews.

Build a reviewed Core Python project that combines files, OOP, collections, exceptions, database/API basics, tests and documentation. Choose a file organiser, expense tracker, student records system, API data collector, log analyser or automation toolkit. Final review covers code structure, README quality, GitHub presentation, viva-style explanation, resume points, mock technical questions and next-path counselling for Django, FastAPI, Data Science, Machine Learning or GenAI.