This PR adds all Python scripts and exercises created while completing Chapter 4 and 5 from [Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud] - link to the O'Reilly book can be found here - https://www.oreilly.com/library/view/intro-to-python/9780135404799/

Includes the below infos:

Learned the followings in chapter4:

• Create custom functions.
• Import and use Python Standard Library modules, such as random and math, to reuse code and avoid “reinventing the wheel.”
• Pass data between functions.
• Generate a range of random numbers.
• Simulation techniques using random-number generation.
• Pack values into a tuple and unpack values from a tuple.
• Return multiple values from a function via a tuple.
• Understand how an identifier’s scope determines where in your program you can use it.
• Create functions with default parameter values.
• Call functions with keyword arguments.
• Create functions that can receive any number of arguments.
• Use methods of an object.

Learned the followings in chapter5:

• Create and initialize lists and tuples.
• Refer to elements of lists, tuples and strings.
• Sort and search lists, and search tuples.
• Pass lists and tuples to functions and methods.
• Use list methods to perform common manipulations, such as searching for items, sorting a list, inserting items and removing items.
• Use additional Python functional-style programming capabilities, including lambdas and the functional-style programming operations filter, map and reduce.
• Use functional-style list comprehensions to create lists quickly and easily, and use generator expressions to generate values on demand.
• Use two-dimensional lists.
• Enhance analysis and presentation skills with the Seaborn and Matplotlib visualization libraries.

Be aware that all scripts run correctly on Python 3.12+.