Python Fundamentals
The course teaches Python fundamentals—data types, variables, control flow, functions, modules, data structures, file I/O, and exception handling—while introducing basic object‑oriented design, enabling learners to write clean, functional code.
60
Minutes
48
Questions
70/100
Passing Score
Who Should Take This
Students, aspiring developers, data analysts, and technical professionals with little to no programming background who want to build a solid Python foundation. It suits recent graduates, career changers, or self‑taught enthusiasts aiming to automate tasks, analyze data, or prepare for advanced software‑engineering roles.
Course Outline
1Data Types and Variables 3 topics
Numeric Types and Variables
- Identify Python's built-in numeric types including int, float, and complex and describe their memory representation differences
- Explain how Python handles dynamic typing and describe the relationship between objects, references, and the id() function
- Implement variable assignments using augmented operators and multiple assignment syntax to manipulate numeric values in calculations
- Implement the walrus operator (:=) for assignment expressions within conditions and comprehensions and describe how it reduces redundant computations
Strings and Text Processing
- Identify string creation methods including single quotes, double quotes, triple quotes, and raw strings and describe when each is appropriate
- Implement string manipulation using slicing, concatenation, f-strings, and common string methods such as split, join, strip, and replace
- Analyze the immutability of strings and evaluate the performance implications of repeated string concatenation versus join operations
Booleans, Type Conversion, and Mutability
- Describe the behavior of boolean values True and False and explain how Python evaluates truthiness for different data types including empty containers and zero values
- Implement type conversion between strings, integers, floats, and booleans using built-in conversion functions and handle potential ValueError exceptions
- Compare mutable and immutable types in Python and analyze how mutability affects variable aliasing and function argument passing behavior
2Control Flow 3 topics
Conditional Statements
- Identify the syntax and semantics of if, elif, and else statements and describe how Python evaluates compound boolean conditions with and, or, and not operators
- Implement nested conditional logic to solve multi-branch decision problems such as grade classification or tax bracket calculation
- Evaluate the use of match-case structural pattern matching introduced in Python 3.10 and compare its expressiveness to chained if-elif blocks
- Implement guard clauses and early returns to flatten nested conditional logic and improve function readability
Loops and Iteration
- Describe the behavior of for loops iterating over sequences, ranges, and enumerate and explain the role of the iterator protocol
- Implement while loops with sentinel values and break/continue statements to control iteration flow in input validation scenarios
- Implement nested loops with early termination using break and else clauses to solve matrix traversal and search problems
Comprehensions
- Implement list comprehensions with filtering conditions to transform and select elements from sequences in a single expression
- Implement dictionary and set comprehensions to construct mappings and unique collections from iterable sources
- Analyze the readability and performance trade-offs between comprehensions and equivalent explicit loop constructions for data transformation tasks
3Functions and Modules 4 topics
Function Definition and Parameters
- Describe the syntax for defining functions with def, including positional parameters, default values, and return statements
- Implement functions using *args and **kwargs to accept variable numbers of positional and keyword arguments
- Explain the difference between positional-only, keyword-only, and positional-or-keyword parameters using the / and * syntax separators
Scope and Closures
- Describe Python's LEGB scope resolution rule and explain how local, enclosing, global, and built-in scopes interact during name lookup
- Implement closures that capture variables from enclosing scopes and explain the behavior of the nonlocal keyword
- Analyze common scoping pitfalls including the mutable default argument trap and late-binding closures in loops
Higher-Order Functions and Decorators
- Implement lambda expressions for simple inline functions and use them as arguments to higher-order functions like map, filter, and sorted
- Explain how decorators work as higher-order functions that wrap other functions and implement a simple decorator with functools.wraps
- Analyze the use of type hints and annotations in function signatures and evaluate their role in code documentation and static analysis tools
- Implement generator functions using yield to produce values lazily and explain how generators conserve memory when processing large datasets compared to returning lists
Modules and Packages
- Describe the Python module and package system including __init__.py, relative imports, and the module search path in sys.path
- Implement code organization using modules and packages with proper import statements including from-import and aliased imports
- Evaluate the trade-offs between importing specific names versus wildcard imports and analyze how circular imports arise and can be resolved
4Data Structures 4 topics
Lists and Tuples
- Identify list operations including append, extend, insert, pop, and sort and describe their time complexity characteristics
- Implement list slicing with start, stop, and step parameters to extract, reverse, and copy subsequences from lists
- Compare lists and tuples in terms of mutability, performance, hashability, and appropriate use cases such as dictionary keys and function return values
Dictionaries
- Describe dictionary creation methods including literals, dict(), and dictionary comprehensions and explain how keys must be hashable
- Implement dictionary operations including get with default values, setdefault, update, and iteration over keys, values, and items
- Analyze the average-case O(1) lookup performance of dictionaries and evaluate when to use defaultdict, Counter, or OrderedDict from collections
- Implement dictionary merge operators (| and |=) introduced in Python 3.9 and compare them with ChainMap and unpacking for combining multiple dictionaries
Sets
- Describe set creation, membership testing, and the requirement that set elements be hashable and unique
- Implement set operations including union, intersection, difference, and symmetric difference to solve data deduplication and comparison problems
- Evaluate when to use sets versus lists for membership testing and analyze the performance difference between O(1) set lookup and O(n) list search
Tuples and Named Tuples
- Implement tuple packing and unpacking including starred assignment to distribute sequence elements across multiple variables
- Implement named tuples using collections.namedtuple or typing.NamedTuple to create lightweight immutable data records with named fields
- Analyze when to use tuples versus lists versus named tuples versus dataclasses for structured data and evaluate each option's trade-offs in readability, mutability, and performance
5File I/O and Exceptions 3 topics
File Reading and Writing
- Describe file opening modes including read, write, append, and binary and explain the importance of the with statement for automatic resource cleanup
- Implement text file reading and writing using open(), read(), readline(), readlines(), and write() with proper encoding specification
- Implement CSV file reading and writing using the csv module including DictReader and DictWriter for structured tabular data processing
- Implement JSON serialization and deserialization using the json module including handling of nested structures and custom encoding
- Analyze encoding issues when reading and writing text files with non-ASCII content and evaluate when to specify encoding='utf-8' versus platform-dependent defaults
Exception Handling
- Describe the exception hierarchy in Python including BaseException, Exception, and common built-in exceptions like ValueError, TypeError, and KeyError
- Implement try-except-else-finally blocks to handle exceptions gracefully and ensure cleanup code executes regardless of error conditions
- Implement custom exception classes by subclassing Exception and design exception hierarchies appropriate to application-specific error domains
- Analyze the trade-offs between EAFP (Easier to Ask Forgiveness than Permission) and LBYL (Look Before You Leap) error handling philosophies in Python
File System Navigation
- Implement pathlib.Path operations for cross-platform file system navigation including joining paths, checking existence, and listing directory contents
- Compare pathlib and os.path approaches to file system operations and evaluate the advantages of the object-oriented pathlib interface
6Object-Oriented Programming Basics 4 topics
Classes and Objects
- Describe the syntax for defining classes with __init__, instance attributes, and methods and explain the role of self as the instance reference
- Implement classes with instance methods, class methods using @classmethod, and static methods using @staticmethod and explain when each is appropriate
- Implement the @property decorator to create managed attributes with getter, setter, and deleter methods for controlled access to instance state
Inheritance and super()
- Describe single inheritance in Python and explain how child classes extend and override parent class methods using super()
- Implement inheritance hierarchies with method overriding and demonstrate the use of super() to invoke parent class constructors and methods
- Analyze the Method Resolution Order (MRO) in Python and evaluate how it resolves method lookup conflicts in diamond inheritance scenarios
- Implement abstract base classes using the abc module with @abstractmethod to enforce method implementation contracts in subclasses
Dunder Methods and Dataclasses
- Implement dunder methods including __str__, __repr__, __eq__, __lt__, and __len__ to customize object behavior and integrate with Python built-in operations
- Implement the __iter__ and __next__ dunder methods to create custom iterable objects that work with for loops and other iteration contexts
- Implement dataclasses using the @dataclass decorator to reduce boilerplate for classes that primarily store data and compare them to regular classes and named tuples
Encapsulation and Polymorphism
- Describe encapsulation conventions in Python including name mangling with double underscores and the single underscore convention for protected members
- Analyze the concept of duck typing in Python and evaluate how it differs from interface-based polymorphism in statically typed languages
- Evaluate the principles of composition versus inheritance and analyze when to favor has-a relationships over is-a relationships in Python class design
Exam Structure
Question Types
- Multiple Choice
- Multiple Response
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
72 Activity Formats
Scope
Included Topics
- Python 3.12+ syntax and semantics, built-in data types, control flow structures, function definition and scope, core data structures (lists, tuples, dicts, sets), file I/O with text and structured formats, exception handling, and object-oriented programming fundamentals including classes, inheritance, and dunder methods
Not Covered
- Web frameworks (Django, Flask, FastAPI)
- Async programming (asyncio, async/await)
- Advanced metaprogramming (metaclasses, descriptors beyond @property)
- Third-party libraries (NumPy, Pandas, requests)
- C extensions and CPython internals
- Concurrency and parallelism (threading, multiprocessing)
- Database access and ORMs
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