This course introduces experienced Python programmers to some of the language’s more advanced features. If you have been using Python for more than six months, and want to understand and explore the more advanced features of the language — making your programs more expressive and powerful — then this course will help you.

This course introduces the NumPy and Pandas packages for Python, and shows how they can be used to ask and answer a variety of questions involving data analysis. NumPy and Pandas are the foundation of the “PyData stack,” a set of numerical and scientific Python packages that have become extremely popular in recent years. Indeed, some financial institutions have begun to replace certain uses of Excel with Pandas, because of its versatility and power.

Git is a sophisticated, flexible, and high-speed system for software version control. It is free to download and use, and operates on a distributed model, so everyone can create and revert commits on a project, even if they’re disconnected from the Internet. It knows how to handle branches and merges more easily than nearly any other version-control system.

Machine learning is changing the world — from predictive typing on your cellphone, to Amazon’s Alexa voice recognition, to the spam detector in our e-mail programs. This course teaches the basic principles behind machine learning, and how these ideas can be put into practice using Python and its popular “scikit-learn” library.

This is an introduction to Python for experienced programmers. After taking this course, you will be ready to start using Python in your day-to-day work, helping your company to reap the benefits of the Python language.

Python has become extremely popular in the last decade; on Stack Overflow, it is both the most popular programming language and the fastest-growing one. However, using Python well requires practice and experience. Its syntax might be clear and straightforward, but it takes time to learn how and when to use different techniques, which idioms are appropriate in different contexts, and how to combine techniques to solve real-world problems.

The "Practical Python Programming" course is an intermediate-level Python course aimed at individuals with some programming experience. It focuses on teaching Python through the practical problem of manipulating data, particularly stock market data, and introduces other programming topics such as data analysis, plotting, web programming, and software design. The course provides hands-on exercises, emphasizes the use of real-world examples, and covers essential concepts like functions, classes, generators, testing, and package organization.

Python is a popular open-source language used in a wide variety of domains — school instruction, academic research, numeric analysis, web applications, and system administration. Because of its readability, Python is considered to be an excellent first language with which to learn programming. Indeed, many universities now use Python in their introductory courses.

Python is used in a wide variety of settings. One popular use is as a language for carrying out a variety of system-administration tasks — from carrying out bulk file operations to monitoring systems to provisioning new servers. In particular, many Unix administrators have started to use Python instead of the traditional Unix shell.

Python has become extremely popular in the last decade; on Stack Overflow, it is both the most popular programming language and the fastest-growing one. However, using Python well requires practice and experience. Its syntax might be clear and straightforward, but it takes time to learn how and when to use different techniques, which idioms are appropriate in different contexts, and how to combine techniques to solve real-world problems.

Regular expressions (“regexps”) make it possible to find patterns inside of text. Whether you’re trying to find URLs in a document, IP addresses in a log file, or telephone numbers in an address book, regular expressions can be invaluable — and thus are included in most modern programming languages, such as Python, Ruby, and JavaScript, as well as .NET, Java, and C++.

From individual hobbyists to the largest corporations, just about everyone agrees that it’s important to test your code. But which testing framework should you use? In the Python world, “pytest” has rocketed to popularity in the last few years — largely because of how easy it is to use and integrate into your software-testing framework and workflow.