Computational Tools¶
This text uses the Python 3 programming language, along with a standard set of numerical and data visualization tools that are used widely in commercial applications, scientific experiments, and open-source projects. Python has recruited enthusiasts from many professions that use data to draw conclusions. By learning the Python language, you will join a million-person-strong community of software developers and data scientists.
A Python program can be executed by any computer, regardless of its manufacturer or operating system, provided that support for the language is installed.
You will see many pages in this site with Python code that can be run. We encourage you to run this code, and experiment with it, to make sure you understand what the code is doing.
One way of doing this, is to click on the “Interact” links at the top of these pages. This will start up a computer in the cloud that can run the code. Your web-browser acts as an interface to that machine, and you do not need to install anything on your computer.
You will also see a “Download notebook” link at the top of these pages. This prompts you to download the notebook, so you can run it on your own computer. To do this, you should install the version of Python and its accompanying libraries that will match this text. One way of doing that is the Anaconda distribution that packages together the Python 3 language interpreter, IPython libraries, and the Jupyter notebook environment.
This text includes a complete introduction to all of these computational tools. You will learn to write programs, generate images from data, and work with real-world data sets that are published online.
Note
This page has content from the Computational_Tools notebook of an older version of the UC Berkeley data science course. See the Berkeley course section of the license file.