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.
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.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
70 learning goals
1
Data 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
2
Control 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
3
Functions 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
4
Data 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
5
File 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
6
Object-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
Hands-On Labs
15 labs
~335 min total
Console Simulator
Code Sandbox
Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.
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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