Netherlands eScience Center

Online

May 17-20, 2021

9:00-12:30

Instructors: Dafne van Kuppevelt, Sven van der Burg, Jens Wehner, Berend Weel, Florian Huber

Helpers: Fakhereh Alidoost

General Information

Where: This training will take place online. The instructors will provide you with the information you will need to connect to this meeting.

When: May 17-20, 2021.

Requirements: Participants must have access to a computer 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 dedicated to providing a positive and accessible learning environment for all. Please notify the instructors in advance of the workshop if you require any accommodations or if there is anything we can do to make this workshop more accessible to you.

Contact: Please email training@esciencecenter.nl for more information.


Code of Conduct

Participants are expected to follow those guidelines:

Schedule

Parallel sessions.

These are the parallel hands-on workshops on Tuesday, Wednesday, & Thursday of the SSI-ML kickoff week.

Day 2. Tuesday May 18th: Introduction to Version Control with Git.

09:30 Version Control with Git
10:15 Coffee break
10:30 Version Control with Git (Continued)
11:30 Coffee break
11:45 Version Control with Git (Continued)
12:30 END

Day 2. Tuesday May 18th: Collaboration with Git and Github (basic Git knowledge needed).

09:30 Collaboration with Git and GitHub
10:15 Coffee break
10:30 Collaboration with Git and GitHub
11:30 Coffee break
11:45 Collaboration with Git and GitHub
12:30 END

Day 3. Wednesday May 19th: Learning to program and analyse data with Python

09:30 Introduction to Python
10:15 Coffee break
10:30 Introduction to Python
11:30 Coffee break
11:45 Starting with Data in Python
12:30 END

Day 3. Wednesday May 19th: Introduction to Machine Learning with Python and scikit-learn

09:30 Intro and data preparation
10:20 Coffee break
10:30 Pipelines in scikit-learn
11:20 Coffee break
11:30 Cross validation and model selection
12:30 END

Day 4. Thursday May 20th: Learning to program and analyse data with Python

09:30 Starting with Data in Python
10:15 Coffee break
10:30 Indexing, Slicing and Subsetting DataFrames in Python
11:30 Coffee break
11:45 Indexing, Slicing and Subsetting DataFrames in Python
12:30 END

Day 4. Thursday May 20th: Introduction to Deep Learning with Python and Keras

09:30 Introduction to Deep Learning and Keras
10:20 Coffee break
10:30 Creating a neural network for classification
11:20 Coffee break
11:30 Monitoring the training process
12:30 END

Setup

To participate in this 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.

Install the videoconferencing client

If you haven't used Zoom before, go to the official website to download and install the Zoom client for your computer.

Set up your workspace

Like other Carpentries workshops, you will be learning by "coding along" with the Instructors. To do this, you will need to have both the window for the tool you will be learning about (a terminal, RStudio, your web browser, etc..) and the window for the Zoom video conference client open. In order to see both at once, we recommend using one of the following set up options:

This blog post includes detailed information on how to set up your screen to follow along during the workshop.

Instructions for 'Introduction to Version Control with Git' and 'Collaboration with Git and Github'

Installing Git

Git is a version control system that lets you track who made changes to what when and has options for easily updating a shared or public version of your code on github.com. You will need a supported web browser.

You will need an account at github.com for parts of the Git lesson. Basic GitHub accounts are free. We encourage you to create a GitHub account if you don't have one already. Please consider what personal information you'd like to reveal. For example, you may want to review these instructions for keeping your email address private provided at GitHub.

  1. Download the Git for Windows installer.
  2. Run the installer and follow the steps below:
    1. Click on "Next" four times (two times if you've previously installed Git). You don't need to change anything in the Information, location, components, and start menu screens.
    2. From the dropdown menu select "Use the Nano editor by default" (NOTE: you will need to scroll up to find it) and click on "Next".
    3. On the page that says "Adjusting the name of the initial branch in new repositories", ensure that "Let Git decide" is selected. This will ensure the highest level of compatibility for our lessons.
    4. Ensure that "Git from the command line and also from 3rd-party software" is selected and click on "Next". (If you don't do this Git Bash will not work properly, requiring you to remove the Git Bash installation, re-run the installer and to select the "Git from the command line and also from 3rd-party software" option.)
    5. Ensure that "Use the native Windows Secure Channel Library" is selected and click on "Next".
    6. Ensure that "Checkout Windows-style, commit Unix-style line endings" is selected and click on "Next".
    7. Ensure that "Use Windows' default console window" is selected and click on "Next".
    8. Ensure that "Default (fast-forward or merge) is selected and click "Next"
    9. Ensure that "Git Credential Manager Core" is selected and click on "Next".
    10. Ensure that "Enable file system caching" is selected and click on "Next".
    11. Click on "Install".
    12. Click on "Finish" or "Next".
  3. If your "HOME" environment variable is not set (or you don't know what this is):
    1. Open command prompt (Open Start Menu then type cmd and press Enter)
    2. Type the following line into the command prompt window exactly as shown:

      setx HOME "%USERPROFILE%"

    3. Press Enter, you should see SUCCESS: Specified value was saved.
    4. Quit command prompt by typing exit then pressing Enter

This will provide you with both Git and Bash in the Git Bash program.

Video Tutorial

For macOS, install Git for Mac by downloading and running the most recent "mavericks" installer from this list. Because this installer is not signed by the developer, you may have to right click (control click) on the .pkg file, click Open, and click Open on the pop up window. After installing Git, there will not be anything in your /Applications folder, as Git is a command line program. For older versions of OS X (10.5-10.8) use the most recent available installer labelled "snow-leopard" available here.

Video Tutorial

If Git is not already available on your machine you can try to install it via your distro's package manager. For Debian/Ubuntu run sudo apt-get install git and for Fedora run sudo dnf install git.

Instructions for 'Introduction to Programming and Data Analysis with Python'

Data

Here is a zip file with data for the Python lesson

Clicking the download link will automatically download all of the files to your default download directory as a single compressed (.zip) file. To expand this file, double click the folder icon in your file navigator application (for Macs, this is the Finder application).

For a full description of the data used in this workshop see this page.

Installing Python

Python is a popular language for research computing, and great for general-purpose programming as well. Installing all of its research packages individually can be a bit difficult, so we recommend Anaconda, an all-in-one installer.

Regardless of how you choose to install it, please make sure you install Python version 3.x (e.g., 3.6 is fine).

We will teach Python using Jupyter Lab, a programming environment that runs in a web browser (Jupyter Lab will be installed by Anaconda). 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 (some older browsers, including Internet Explorer version 9 and below, are not).

  1. Open https://www.anaconda.com/products/individual#download-section with your web browser.
  2. Download the Anaconda for Windows installer with Python 3. (If you are not sure which version to choose, you probably want the 64-bit Graphical Installer Anaconda3-...-Windows-x86_64.exe)
  3. Install Python 3 by running the Anaconda Installer, using all of the defaults for installation except make sure to check Add Anaconda to my PATH environment variable.

Video Tutorial

  1. Open https://www.anaconda.com/products/individual#download-section with your web browser.
  2. Download the Anaconda Installer with Python 3 for macOS (you can either use the Graphical or the Command Line Installer).
  3. Install Python 3 by running the Anaconda Installer using all of the defaults for installation.

Video Tutorial

  1. Open https://www.anaconda.com/products/individual#download-section with your web browser.
  2. Download the Anaconda Installer with Python 3 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.)
  3. Open a terminal window and navigate to the directory where the executable is downloaded (e.g., `cd ~/Downloads`).
  4. Type
    bash Anaconda3-
    and then press Tab to autocomplete the full file name. The name of file you just downloaded should appear.
  5. Press Enter (or Return depending on your keyboard). 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 (or Return) to approve the default location for the files. Type yes and press Enter (or Return) to prepend Anaconda to your PATH (this makes the Anaconda distribution the default Python).
  6. Close the terminal window.

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Instructions for 'Introduction to Machine Learning with scikit-learn'

For this workshop, you need to have Python 3 and Jupyter lab installed. See the instructions for the Intro to Python workshop on how to install Python with Anaconda.

In addition, you need the following python packages:

These packages can be installed through anaconda following these instructions Or through the command line:

conda install pandas scikit-learn seaborn

Instructions for 'Introduction to Deep Learning with Keras'

For this workshop, you need to have Python 3 and Jupyter lab installed. See the instructions for the Intro to Python workshop on how to install Python with Anaconda.

In addition, you need the following python packages:

These packages can be installed through anaconda following these instructions Or through the command line:

conda install pandas scikit-learn seaborn tensorflow

Data

For the Deep Learning with Keras workshop, you need to download the Weather prediction dataset.