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Python Programming

The course teaches Python programming fundamentals, covering variables, types, expressions, control flow, data structures, functions, and object‑oriented programming, enabling learners to build clean, maintainable code for real‑world tasks.

Who Should Take This

It is ideal for recent graduates, career changers, or junior developers who have little to no Python experience but want to transition into software development or data‑analysis roles. Participants aim to master core language concepts, write idiomatic Python, and confidently contribute to team projects.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

53 learning goals
1 Variables, Types, and Expressions
2 topics

Core Data Types and Variables

  • Identify Python's core built-in types (int, float, str, bool, None) and declare variables using dynamic typing, explaining that Python variables are references to objects rather than fixed-type containers.
  • Apply arithmetic operators (+, -, *, /, //, %, **), comparison operators (==, !=, <, >, <=, >=), and logical operators (and, or, not) to evaluate expressions, predicting the result type and value including integer vs. float division behavior.
  • Explain type conversion using int(), float(), str(), and bool(), predicting the output of explicit conversions and identifying when implicit type coercion occurs versus when a TypeError is raised.
  • Use input() and print() for basic console I/O, applying f-string formatting with format specifiers for alignment, padding, decimal precision, and type-specific formatting.

Strings and String Manipulation

  • Apply string methods (split, join, strip, replace, find, upper, lower, startswith, endswith, count) to manipulate text data, chaining multiple method calls to transform strings.
  • Use string indexing and slicing (including negative indices and step values) to extract substrings, and explain the immutability of strings by predicting which operations create new string objects.
  • Apply basic regular expressions using the re module (match, search, findall, sub) to validate and extract patterns from text, constructing patterns with character classes, quantifiers, and groups.
2 Control Flow
2 topics

Conditionals and Boolean Logic

  • Write if, elif, and else statements to implement multi-branch decision logic, predicting execution flow for nested conditionals with compound boolean expressions using and, or, and not.
  • Explain Python's truthiness rules (falsy values: None, 0, 0.0, '', [], {}, set()) and apply short-circuit evaluation to write concise guard expressions and conditional assignments.

Loops and Iteration

  • Implement for loops using range() and iterable objects (strings, lists, dicts) and while loops with appropriate termination conditions, tracing loop execution to predict output.
  • Apply break, continue, and pass within loops, and use the for/else and while/else constructs, explaining when the else clause executes versus when it is skipped.
  • Use enumerate() and zip() to iterate with indices and over parallel sequences, and apply nested loops to process two-dimensional data structures.
3 Data Structures
2 topics

Lists and Tuples

  • Create and manipulate lists using append, insert, remove, pop, sort, reverse, and index methods, and access elements using indexing and slicing.
  • Write list comprehensions with conditional filtering and nested iteration, converting between explicit for-loop accumulation patterns and equivalent comprehension syntax.
  • Explain the differences between lists and tuples (mutability, hashability, performance, semantic intent) and choose the appropriate type for a given use case such as function return values or dictionary keys.
  • Analyze the difference between shallow and deep copies of lists (copy(), copy.deepcopy()), predicting mutation behavior when lists contain mutable nested objects.

Dictionaries and Sets

  • Create and manipulate dictionaries using literal syntax, dict(), get(), setdefault(), update(), keys(), values(), items(), and del, handling KeyError with get() or membership testing.
  • Write dictionary comprehensions and use collections.defaultdict and collections.Counter to solve common aggregation and counting problems efficiently.
  • Create and manipulate sets using add, remove, discard, union, intersection, difference, symmetric_difference, and issubset/issuperset, applying set operations to solve deduplication and membership problems.
  • Explain hashability requirements for dictionary keys and set elements, predicting which types are hashable and designing data structures that use frozenset or tuples as keys.
4 Functions
2 topics

Function Definition and Parameters

  • Define functions using def with positional, keyword, and default parameters, and return single or multiple values using return and tuple unpacking.
  • Use *args and **kwargs to accept variable numbers of positional and keyword arguments, and explain argument unpacking when calling functions with * and ** operators.
  • Explain the LEGB (Local, Enclosing, Global, Built-in) scope resolution rule and predict variable lookup behavior in nested functions, distinguishing between local assignment and global/nonlocal declarations.
  • Identify and avoid the mutable default argument pitfall (e.g., def f(x=[])) by explaining why default arguments are evaluated once at function definition time and using None sentinel instead.

Lambdas and Higher-Order Functions

  • Write lambda expressions for simple one-expression functions and use them as arguments to built-in higher-order functions (sorted, map, filter, min, max) with a key parameter.
  • Explain closures by demonstrating how an inner function captures variables from its enclosing scope, and use closures to create function factories that generate parameterized behavior.
5 Object-Oriented Programming
2 topics

Classes and Objects

  • Define classes with __init__, instance attributes, and instance methods, creating and using objects that encapsulate state and behavior.
  • Distinguish between instance attributes, class attributes, instance methods, class methods (@classmethod), and static methods (@staticmethod), predicting attribute lookup behavior and choosing the appropriate method type.
  • Implement dunder/magic methods (__str__, __repr__, __eq__, __lt__, __len__, __getitem__, __contains__) to customize object behavior for printing, comparison, iteration, and membership testing.
  • Use @property decorators to create managed attributes with getter, setter, and deleter methods, implementing validation logic and computed properties.

Inheritance and Polymorphism

  • Implement single inheritance using class Child(Parent) syntax, calling super().__init__() to initialize the parent class, and override methods to specialize subclass behavior.
  • Explain the method resolution order (MRO) in multiple inheritance scenarios using C3 linearization, predicting which method implementation is called when a class inherits from multiple parents.
  • Apply duck typing and isinstance()/issubclass() to write polymorphic code that operates on objects based on their behavior rather than their explicit type, following Python's 'ask forgiveness not permission' convention.
6 Error Handling and File I/O
2 topics

Exceptions and Error Handling

  • Implement try/except/else/finally blocks to handle exceptions, catching specific exception types (ValueError, TypeError, KeyError, IndexError, FileNotFoundError) and explaining the execution flow through each clause.
  • Raise exceptions using raise and define custom exception classes that inherit from Exception, designing exception hierarchies for domain-specific error reporting.
  • Evaluate exception handling strategies (EAFP vs. LBYL) and choose the Pythonic approach for a given scenario, avoiding overly broad except clauses that mask bugs.

File Operations

  • Read and write text files using open() with context managers (with statement), applying read(), readline(), readlines(), and write()/writelines() methods with appropriate file modes ('r', 'w', 'a', 'x').
  • Process structured data files using the csv module (reader, writer, DictReader, DictWriter) and the json module (load, dump, loads, dumps) for serialization and deserialization.
  • Use pathlib.Path for cross-platform file system operations (joining paths, checking existence, listing directory contents, reading/writing files) and explain its advantages over os.path string manipulation.
7 Iterators, Generators, and Decorators
2 topics

Iterators and Generators

  • Explain the iterator protocol (__iter__ and __next__) and implement a custom iterator class, distinguishing between iterables (objects with __iter__) and iterators (objects with __next__).
  • Write generator functions using yield to produce values lazily, and use generator expressions as memory-efficient alternatives to list comprehensions for large data processing.
  • Apply itertools functions (chain, zip_longest, islice, product, combinations, permutations, groupby) to compose complex iteration patterns without materializing intermediate lists.

Decorators

  • Explain how decorators work as functions that take a function and return a modified function, implementing a basic decorator that wraps a function with additional behavior (logging, timing).
  • Write decorators that accept arguments using the nested-function pattern, apply functools.wraps to preserve the decorated function's metadata, and stack multiple decorators understanding execution order.
8 Modules, Packages, and Type Hints
2 topics

Modules and Packages

  • Import modules using import, from...import, and aliased imports (as), explaining how Python locates modules through sys.path and the difference between absolute and relative imports.
  • Create a Python package with __init__.py, organize code into modules, and use the if __name__ == '__main__' guard to write scripts that are both importable and executable.
  • Create and manage virtual environments using python -m venv, install packages with pip, generate and use requirements.txt for reproducible dependency management.

Type Hints and Annotations

  • Add type annotations to function parameters and return values using built-in types and typing module constructs (List, Dict, Optional, Union, Tuple, Any, Callable), and explain that type hints are not enforced at runtime.
  • Use modern Python 3.10+ type hint syntax (X | Y instead of Union[X, Y], built-in list[int] instead of List[int]) and apply type aliases and TypeVar for generic function signatures.
9 Standard Library Essentials
2 topics

Collections and Functools

  • Apply collections.Counter, collections.defaultdict, collections.namedtuple, and collections.deque to solve data aggregation, grouping, and record-keeping problems more idiomatically than plain dicts and lists.
  • Use functools.lru_cache for memoization, functools.partial for partial function application, and functools.reduce for cumulative computation, choosing the right tool for each scenario.

Date, Time, and System Utilities

  • Create, format, and perform arithmetic on dates and times using the datetime module (date, time, datetime, timedelta), parsing date strings with strptime and formatting with strftime.
  • Use os.environ for environment variables, sys.argv for command-line arguments, and argparse.ArgumentParser to build robust CLI tools with positional and optional arguments, help text, and type validation.

Scope

Included Topics

  • Python 3.12+ programming fundamentals covering variables, data types, operators, expressions, and basic I/O using input() and print().
  • Control flow: if/elif/else statements, boolean logic, comparison operators, while loops, for loops, range(), break, continue, and pass.
  • Data structures: lists, tuples, sets, dictionaries, list comprehensions, dictionary comprehensions, set comprehensions, and nested data structures.
  • Functions: definition with def, parameters (positional, keyword, default, *args, **kwargs), return values, scope (LEGB rule), closures, and lambda expressions.
  • Object-oriented programming: classes, __init__, instance and class attributes, methods (instance, class, static), inheritance, method resolution order (MRO), super(), encapsulation, properties (@property), and dunder/magic methods (__str__, __repr__, __eq__, __lt__, __len__, __getitem__).
  • String manipulation: f-strings, string methods (split, join, strip, replace, find, format), regular expressions basics (re module: match, search, findall, sub), and string formatting.
  • File I/O: open(), read/write modes, context managers (with statement), reading and writing text and CSV files, pathlib.Path basics.
  • Error handling: try/except/else/finally, raising exceptions, custom exception classes, and common built-in exceptions (ValueError, TypeError, KeyError, IndexError, FileNotFoundError).
  • Modules and packages: import system, creating modules, __name__ == '__main__', pip, virtual environments (venv), requirements.txt, and basic package structure (__init__.py).
  • Iterators and generators: the iterator protocol (__iter__, __next__), generator functions (yield), generator expressions, and itertools basics (chain, zip_longest, islice, product).
  • Decorators: function decorators, decorator syntax (@), functools.wraps, decorators with arguments, and common standard library decorators (@staticmethod, @classmethod, @property).
  • Type hints: basic type annotations, typing module (List, Dict, Optional, Union, Tuple, Any, Callable), and mypy basics.
  • Standard library highlights: collections (Counter, defaultdict, namedtuple, deque), functools (reduce, partial, lru_cache), os and sys basics, json module, datetime module, and argparse.

Not Covered

  • Advanced concurrency and parallelism (asyncio, threading, multiprocessing) beyond basic conceptual awareness.
  • Web frameworks (Django, Flask, FastAPI) and web development patterns.
  • Data science libraries (NumPy, pandas, matplotlib, scikit-learn) beyond incidental references.
  • Database ORMs (SQLAlchemy, Django ORM) and database connectivity.
  • C extensions, ctypes, Cython, and CPython internals.
  • Deployment, containerization, CI/CD pipelines, and production infrastructure.

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