Python Bootcamp: Zero to Hero

Learn variables, data types, and expressions in Python, including integers, floats, strings, and booleans, with dynamic typing. Practice descriptive names and snake_case for readable code.

Explore variables, data types, and expressions in python by creating and manipulating string, integer, boolean, and float values, printing results, and building a simple order-tracking example.

Master arithmetic and logical operators in Python, from basic math and compound assignments to comparison and logical operations used for decisions and conditions.

Utilizes arithmetic and compound assignment to compute total price, applies a member discount via conditional logic, and uses comparison and logical operators to gate access by age and ID.

Demonstrates using f-strings to embed variables and expressions inside a Python string, improving readability by placing the code inside curly braces and performing simple calculations.

Master Python conditionals with if, elif, and else to control program flow. Explore top-to-bottom evaluation and how expressions and logical operators drive decisions, from login checks to cart status.

Master conditionals in Python by writing if, elif, and else blocks with indentation, using real world examples like login checks and payment status.

Learn to use loops in Python to repeat actions efficiently using for, while, and range; print numbers 1 to 10, iterate lists, or retry until success.

Explore Python loops with for, while, and range through practical examples like a grocery list and order numbers. Learn end exclusivity, condition updates, and how to avoid endless loops.

Explore nested control flows and break and continue in Python, using a vending machine example that checks item presence and stock to determine dispensing or out of stock.

Demonstrates using comments to explain Python code and debugging techniques in Visual Studio Code, including adding breakpoints, stepping through with F10, inspecting variables, and fixing an undefined variable.

Discover how Python functions organize logic into named blocks and reduce repetition. Learn how return values pass data between code blocks and enable reuse.

Explore how to define and call Python functions, return values, and reuse code through practical examples like coffee making and applying discounts, with parameters, return statements, and printing.

Explore how to make Python functions flexible with default parameter values, and handle any number of positional and keyword arguments using *args and **kwargs, while understanding dictionaries for optional settings.

Learn to define Python functions with default values, *args and **kwargs, using examples like a delivery fee, a billing system, and dynamic user profiles.

Discover how nested functions in Python organize related logic, keep inner code local to an outer function, and decide when to use a separate function for reusable helper logic.

Define a function inside another to format a full name by stripping spaces, capitalizing names, and returning the formatted result from the outer function after calling the inner function.

Explore how lambda functions in Python create small anonymous functions for quick tasks, doubling a number or adding two inputs, often used with map, filter, or sorted.

Explore how the pass keyword helps Python code run smoothly while you sketch functions and loops. See how pass prevents indentation errors and signals later logic with comments.

Explore object-oriented programming in Python through classes, objects, attributes, and methods, using a vehicle blueprint example with Toyota and Honda objects to illustrate state and behavior.

Build a simple Python vehicle class, create two objects, and use attributes and methods with self to print each car’s brand and color and start its engine.

Learn how inheritance and polymorphism in Python let you build classes from existing ones, reuse code, and make objects respond differently to the same method call for scalable, reusable programs.

Explore inheritance and polymorphism in python with a vehicles example, where car and bike derive from vehicle and each implements its own Startengine method, while sharing the parent constructor.

Explore dunder methods and encapsulation in Python, showing how double-underscore methods like init and str guide object behavior and how encapsulation protects internal details.

Learn Python encapsulation with a bank account class featuring a private balance, deposit and withdraw methods, and a __str__ dunder method for safe, readable printing.

Explore Python's core data structures—lists, tuples, sets, and dictionaries—showing when to use each for ordered, mutable collections; fixed data; unique elements; and key-value labeling.

Explore Python's core data structures by using lists, tuples, sets, and dictionaries, practicing indexing, adding, updating, and removing items, and note that tuples can't be updated.

Master nesting and iteration patterns to organize complex data with nested lists and dictionaries, and to design effective nested loops that control data access.

Learn nesting and nested loops with iteration patterns in Python by processing a list of student records, printing names and marks, computing averages, and classifying outcomes with conditional logic.

Learn memory efficient operations in Python by processing items one at a time, using generators, and selecting memory-friendly data types like tuples and sets for large data.

Discover memory-efficient Python patterns by generating values with a generator and yield, avoiding large in-memory lists. Choose tuples over lists to save memory in big data tasks.

Learn Python's built-in methods for lists, sets, and dictionaries, including adding, removing, updating, and retrieving values to write shorter, faster, clearer code.

Explore Python's built-in methods for lists, sets, and dictionaries, using append, remove, sort, reverse, add, discard, update, and get to manipulate data efficiently.

Learn how to read and write files in Python and use a context manager to safely open and automatically close the file, saving resources.

Learn to read from and write to files in Python using the with context manager for safe opening and closing. Write, read, and append with modes w, r, and a.

Master Python error handling with try/except, raise, and custom exceptions to prevent crashes, display friendly messages, and enforce specific checks like a password too short.

Learn to use try and except to handle zero division and input errors with friendly messages. Raise custom exceptions for invalid input and catch them to prevent crashes.

Learn how regex in Python powers data extraction and validation by defining patterns to find emails, phone numbers, and dates, and validate formats with concise, pattern-based logic.

Learn to use regex in Python to extract emails and validate phone numbers. Import the re module, create a pattern, and use findall and fullmatch for extraction and ten-digit validation.

Discover how automation in Python saves time and effort by writing scripts that repeat tasks like renaming files, moving folders, copying data, and filling forms, delivering faster, more accurate results.

Automate a file renaming and sorting workflow in the downloads folder by detecting extensions, renaming files with numeric suffixes, creating extension folders, and moving files with OS and shutil.

Learn web automation basics with Selenium in Python by enabling your code to control a real browser to open pages, click buttons, fill forms, and scrape data.

Learn to automate a browser with Selenium and WebDriver Manager, install libraries, open a website, and interact with a search box named q by sending keys and submitting.

Learn to create data frames in Pandas from CSV files, Python dictionaries, and lists, using three basic methods to build tables with rows and columns.

Install pandas and create data frames from csv, dictionary, and list of lists using read_csv and DataFrame, printing and inspecting with head.

Learn to filter, sort, and clean data with pandas by creating a data frame, filtering ages over 30, and using isnull, dropna, fillna, and drop_duplicates.

Group data with pandas to aggregate by department, count names, and compute the average salary per department, printing results and revealing additional functions such as sum, max, and min.

Explore numpy arrays, designed for numbers and faster than lists, and harness vectorized operations to perform elementwise math on entire arrays without loops.

Learn to create NumPy arrays and perform vectorized operations, including elementwise addition and multiplication, without loops, then explore array creation, slicing, and reshaping.

Create, slice, and reshape arrays with numpy from a range to a 1d array, then reshape to 3x4 and flatten back.

Learn to use numpy for math and statistics on 2D arrays, with sum, mean, min, max, std, and axis 0/1 for column and row sums; visualize with matplotlib and seaborn.