KAUST Visualization Core Lab: 01 April 2020

General Information

The KAUST Visualization Lab is hosting an Introduction to Conda for (Data) Scientists workshop. Conda is an open source package and environment management system that runs on Windows, macOS and Linux. Conda installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads, and switches between environments on your local computer. While Conda was created for Python programs it can package and distribute software for any languages such as R, Ruby, Lua, Scala, Java, JavaScript, C/ C++, FORTRAN. This lesson motivates the use of Conda as a development tool for building and sharing project specific software environments that facilitate reproducible (data) science workflows.

This hands-on lesson is part of the Introduction to Data Science Workshop Series being offered by the KAUST Research Computing Core Labs as part of our on-going efforts to build capacity in core data science skills at KAUST. The curriculum of the Introduction to Data Science Workshop Series largely follows the core Software Carpentry curriculum and covers programming in Python, programming in R, shell scripting, version control using Git, and database programming with SQL.

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, faculty and other research staff at KAUST. You don't need to have any previous knowledge of the tools that will be presented at the workshop.

Where: Building 3, level 5, room 5220 (Seaside). Get directions with OpenStreetMap or Google Maps.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below).

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organizers have checked that:

The room is wheelchair / scooter accessible.

Accessible restrooms are available.

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch (using contact details below) and we will attempt to provide them.

Contact: Please email david.pugh@kaust.edu.sa for more information.

Setup

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.

Text Editor

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.

Download the 64-bit Windows installer.

Download the macOS installer.

Download the appropriate installer for your flavour of Linux.

Python

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.

Open https://docs.conda.io/en/latest/miniconda.html with your web browser.
Download the 64-bit Python 3 installer for Windows.
Install Python 3 using all of the defaults for installation except make sure to check Add Miniconda to my PATH environment variable.

Open https://docs.conda.io/en/latest/miniconda.html with your web browser.
Download the 64-bit Python 3 installer for OS X.
Install Python 3 using all of the defaults for installation.

Open https://docs.conda.io/en/latest/miniconda.html with your web browser.
Download the 64-bit Python 3 installer for Linux. The installation requires using the shell. If you aren't comfortable doing the installation yourself stop here and request help at the workshop.
Open a terminal window.
Type
```
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 ~/Downloads
```
Then, try again.
Press Return. You will follow the text-only prompts. To move through the text, press Spacebar. Type yes and press enter to approve the license. Press enter to approve the default location for the files. Type yes and press enter to prepend Miniconda to your PATH (this makes the Miniconda distribution the default Python).
Close the terminal window.

Before	Pre-workshop survey
13:00	Introduction to Conda
14:30	Afternoon break
14:45	Reproducible Software Environments with Conda
16:30	Wrap-up
17:00	END
After	Post-workshop survey

Introduction to Conda for (Data) Scientists