Download the 64-bit Windows installer.
2 March 2021
1:00 pm - 5:00 pm
Instructors: Dr. David R. Pugh
Helpers: Glendon Holst
The KAUST Visualization Lab is hosting an Introduction to Python for Data Science workshop. Visualization lab staff will provide an introduction to Python for data science designed for learners with little or no previous programming experience. Topics covered will include
This hands-on lesson is part of the Introduction to Data Science Workshop Series being offered by KVL as part of our on-going efforts to build capacity in core data science skills both at KAUST and within the Kingdom of Saudi Arabia (KSA).
The workshop curriculum largely follows the curriculum developed by Software Carpentry, a volunteer project dedicated to helping researchers get their work done in less time and with less pain by teaching them basic research computing skills.
This is a live-coding based workshop and learners are expected to work along with the instructor using freely available cloud resources provided by the Binder project.
For more information on what Software Carpentry teaches and why, please see their paper "Best Practices for Scientific Computing".
Who: The course is aimed at graduate students (MSc and PhD), post-docs, scientists, faculty, and industry researchers and practioners at KAUST and within the Kingdom of Saudi Arabia (KSA).
When: 2 March 2021. Add to your Google Calendar.
Contact: Please email email@example.com for more information.
Everyone who participates in Carpentries activities is required to conform to the Code of Conduct.This document also outlines how to report an incident if needed.
We will use this collaborative document for chatting, taking notes, and sharing URLs and bits of code.
Please be sure to complete these surveys before and after the workshop.
To participate in a Software Carpentry workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.
We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.
When you're writing code, it's nice to have a text editor that is optimized for writing code, with features like automatic color-coding of key words. The default text editor on macOS and Linux is usually set to Vim, which is not famous for being intuitive. If you accidentally find yourself stuck in it, hit the Esc key, followed by :+Q+! (colon, lower-case 'q', exclamation mark), then hitting Return to return to the shell.
Visual Studio Code is a source-code editor developed by Microsoft for Windows, Linux and macOS. It includes support for debugging, embedded Git control and GitHub, syntax highlighting, intelligent code completion, snippets, and code refactoring. It is highly customizable, allowing users to change the theme, keyboard shortcuts, preferences, and install extensions that add additional functionality. The source code is free and open source and released under the permissive MIT License.
Python is a popular language for research computing, and great for general-purpose programming as well. While there are many different ways to install Python, we recommend installing the 64-bit Python 3 version of Miniconda.
We will teach Python using the JupyterLab, a programming environment that runs in a web browser. For this to work you will need a reasonably up-to-date browser. The current versions of the Chrome, Safari and Firefox browsers are all supported.
bash Miniconda3-and then press Tab. The name of the file you just downloaded should appear. If it does not, navigate to the folder where you downloaded the file, for example with:
cd ~/DownloadsThen, try again.
yesand press enter to approve the license. Press enter to approve the default location for the files. Type
yesand press enter to prepend Miniconda to your
PATH(this makes the Miniconda distribution the default Python).