Class notes

Important Python Code Lecture Notes

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Course
Cp164

Institution
Wilfrid Laurier University (WLU )

The document that I am presenting is a collection of lecture notes for a course on Python programming. The course is designed for beginners and aims to provide a comprehensive introduction to the Python programming language. The lecture notes cover all of the fundamental concepts of Python, incl...

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Uploaded on December 23, 2022
Number of pages 7
Written in 2022/2023
Type Class notes
Professor(s) David brown
Contains All classes

python
notes

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Working with large datasets and distributed systems

Working with large datasets and distributed systems involves handling and processing large

amounts of data in a distributed manner across multiple computers or servers. This can be

necessary when dealing with datasets that are too large or complex to be processed on a single

machine.

One way to work with large datasets and distributed systems is through the use of Python.

Python is a programming language that is commonly used for data analysis and machine learning

tasks, due to its easy-to-learn syntax and large ecosystem of libraries and tools.

One of the main advantages of using Python for large datasets and distributed systems is its

ability to scale. Python has several libraries and frameworks that allow developers to easily

distribute tasks across multiple machines or servers. For example, the PySpark library can be

used to write distributed programs that can process data stored in Apache Spark clusters, which

are distributed data processing platforms.

Another advantage of Python is its ability to handle large amounts of data efficiently. Python has

several libraries and tools that allow developers to manipulate and analyze large datasets with

minimal memory usage. For example, the Pandas library provides tools for working with large

datasets in a way that is both efficient and easy to use.

, Overall, Python is a powerful tool for working with large datasets and distributed systems. Its

ability to scale and handle large amounts of data make it an ideal choice for data analysis and

machine learning tasks that require distributed processing.

Working with cloud services (e.g. using AWS, GCP, Azure)

Cloud services refer to the delivery of computing resources, such as servers, storage, and

networking, over the internet. Companies such as Amazon Web Services (AWS), Google Cloud

Platform (GCP), and Microsoft Azure offer a range of cloud services for businesses to utilize.

Using these cloud services allows companies to scale their computing resources as needed, rather

than having to invest in and maintain their own physical servers. This can be more cost-effective

and efficient, as the company only pays for the resources they use and doesn't have to worry

about maintenance or upgrading hardware.

One way to work with cloud services is through the use of programming languages such as

Python. Python is a popular choice for cloud development due to its simplicity and flexibility.

Python can be used to write scripts and programs that interact with the cloud services through

APIs (Application Programming Interfaces).

For example, a company using AWS may use Python to automate the process of creating and

managing virtual servers, or to retrieve and analyze data stored in the cloud. Similarly, a

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