Build accurate, engaging, and easy-to-generate data visualizations using the popular programming language Python.

This course offers scientists and engineers (ranging from students of those disciplines to experienced professionals) a dedicated, empowering introduction to Python for scientific and engineering applications. Theoretical astrophysicist and Python enthusiast Michele Vallisneri explains how Python can help you become a better engineer or physicist by making your work more efficient, accurate, and agile. Michele walks you through installing Python for macOS, Windows, and Linux, as well as setting up Jupyter notebooks. He explains how you can make Python fast using NumPy arrays, the SciPy library, Numba, and Cython. Michele then tackles ways to ensure your code is correct with tools for symbolic computation, differential equations, interpolation, and more. He finishes up with ideas to make your computational life easier with Python, including JSON, pandas, HDF5, automation with Python scripts, and scientific workflows with Snakemake.

Do you need to learn Python for your current job, for the job you want, or just for fun? In this course, instructor Ronnie Sheer shows you how you can leverage your existing knowledge of JavaScript to get started programming in Python. Ronnie compares Python and JavaScript, then covers how to set up and run a Python environment. He discusses how to write code that adheres to Python conventions, which can prevent bugs. Ronnie explains some of the basic logic in Python, including the use of indentation, how to write and debug control flow, how to use Python try/except appropriately, and more. He goes over objects and class definitions, then explores Python’s data model feature and mixins class. Ronnie goes into iteration in Python and shows how you can write more concise and elegant code in Python by using zip, enumerate, and list comprehensions. He concludes with a challenge that shows you how to apply your knowledge of JavaScript and Python to improve a small program.

Learn programming with Python in this fun and easy-to-follow course designed for high-school and college-age students.

Expanding your C# knowledge to include Python gives you a competitive advantage in the job marketplace. In this course, Joe Marini helps you translate your knowledge into the Python language. Python and C# share many similarities but are different in important ways. Joe explains the difference between C# and Python comment syntax. Python’s conditional statements and loop control statements are somewhat limited compared to C#. Joe walks you through the differences. Functions are a basic part of most programming languages. Joe shows you how several important functions work in Python. Python supports class definitions, single and multiple inheritance, abstract classes, and custom functionality. Joe compares and contrasts these with C# functionality. Joe shows you how to build and work with Python arrays of various data types, how to declare and use dictionaries in Python, and how to use iterators in Python. In conclusion, Joe discusses common operations like strings and exceptions.

Save time, and make your code more readable and reusable, by learning the most powerful Python functions for data science.

Learn how to build a graphical user interface (GUI) for your Python applications with Tkinter.

Learn how to automate the configuration of network devices with NAPALM, a Python library used to automate and interact with network devices and OSs using a unified API.

Enhance your network programming skills. Learn how to iterate and scale scripts with Netmiko—a Python library for configuring SSH connections to routers and switches.

Learn how to use the object-oriented features of Python, including magic methods and data classes, to write more efficient and effective code.

Strengthen your understanding of Python by tackling a series of hands-on projects. Learn how to translate classic design patterns into Python and create a Python Windows service.

Canned challenge problems are sufficient practice for brand-new Python developers. But if you're an intermediate programmer looking to bring a novel idea to life, you require more nuanced, in-depth instruction and practice. In this course, instructor Barron Stone helps you learn Python the hands-on way, showing you what it takes to bring a Python project to the finish line. He walks you step by step through his entire development process for a single personal Python project, from initial ideation to building the final installer for distribution. As you follow along, you can observe the thought process that Barron follows, giving you a path to follow to create your own projects.

Get a fast-paced introduction to Python. Learn the basics of Python, including data types, functions, and control flow, and decide if it’s the right language for your projects.

Learn how to leverage the pre-built code in the Python Standard Library to make building applications with Python even easier.

Extend your basic knowledge of statistics by building analytics skills using Python and powerful third-party libraries.

Python is a critical skill for network engineers. Learn the very basics of network programming with Python—the theoretical building blocks that will lead to better scripts.

Python and JavaScript are two of the most popular programming languages used by developers, analysts, and data scientists alike. But how do you know which language to use for specific projects or tasks? In this course, longtime instructor and software engineer Julie Nisbet answers this question and provides you with the tools you need to decide between these languages when approaching different projects. Julie starts by exploring the history of each language and how they operate. She then discusses key considerations when picking between each language for a project or task. Julie continues by reviewing key differences between the two programming languages, and closes by exploring various use cases for Python and JavaScript. Upon completing this course, you'll feel prepared to determine whether Python or JavaScript is best for you based on the project at hand.

This course was created by Madecraft. We are pleased to host this training in our library.

Python and R are common programming languages used when working with data. Each language is powerful in its own way; however, it's important that you select the language that will best help you achieve your end result. In this course, data scientist and coding instructor Lavanya Vijayan helps you make this choice, sharing important considerations for using each language in various circumstances. Lavanya starts by going over the background of both languages, as well as the strengths and disadvantages of each in different scenarios. She then walks through the process of working on a data science project and how you'd handle the data at various stages using Python and R. Lavanya then covers how to analyze data using both languages. She rounds out the course by discussing the use cases that play to each language's strengths. By the end of this training, you’ll have the essential information you need to determine whether Python or R is right for you.

This course was created by Madecraft. We are pleased to host this training in our library.

Explore some advanced design patterns as applied to Python. This course helps you examine how these patterns work at the code level by walking through sample scripts.

Learn how to use decorators to add functionality to your Python projects.