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In this tutorial, learn about variables & data types in the Python and how they work
In Python, variables are used to store data values, and they can hold different data types. Here are the most common data types and their corresponding variables:

Integer (int): Variables that hold whole numbers, positive or negative. Example: x = 5

Floating-point (float): Variables that hold decimal numbers. Example: y = 3.14

String (str): Variables that hold text or characters, enclosed in single or double quotes. Example: z = "Hello, world!"

Boolean (bool): Variables that hold a binary value of either True or False. Example: is_sunny = True

List: Variables that hold an ordered sequence of values, separated by commas and enclosed in square brackets. Example: my_list = [1, 2, 3, "four", True]

Tuple: Variables that hold an ordered, immutable sequence of values, separated by commas and enclosed in parentheses. Example: my_tuple = (1, 2, 3, "four", True)

Dictionary: Variables that hold an unordered collection of key-value pairs, separated by colons and enclosed in curly braces. Example: my_dict = {"name": "John", "age": 30, "is_student": True}

Set: Variables that hold an unordered collection of unique elements, enclosed in curly braces. Example: my_set = {1, 2, 3, "four"}

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Benefits
➕ Code organization: Variables help to organize code by assigning values to names, making it easier to understand and manage.

➕ Code readability: Python's use of variables and data types makes the code more readable and understandable, both for the developer and other people who may need to read or modify the code in the future.

➕ Code reusability: By using variables and data types, developers can write code that is reusable in other parts of the program or even in different programs, which saves time and effort.

➕ Error detection: Python's use of strong typing ensures that data types are enforced, which helps to detect errors early on in the development process.

Overall, the use of variables and data types in Python makes coding more efficient, organized, and readable, leading to more effective and maintainable programs.

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To best understand the Data Science with Python course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this training.

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