Help & Info


 Table of contents

Instructors

Evolutionary and behavioural ecologist by training, Software/Data Carpentry instructor, and open source proponent, Marie-Hélène Burle develops and delivers training for researchers on computing tools (R, Python, Julia, Git, Bash scripting, machine learning, parallel scientific programming, HPC) for Simon Fraser University and the Digital Research Alliance of Canada.
Grace Fishbein joined ACENET in 2019 and is based in St. John’s at Memorial University of Newfoundland. She completed her undergraduate degree at Memorial in applied math and physics and then pursued her masters in biomedical physics at Ryerson University. Her masters research involved exploring the design and behaviour analysis of microbubbles and nanobubbles, which are used as contrast agents in ultrasound. This required the development of an algorithm to sort through and differentiate RF data demonstrating bubble behaviour using MATLAB. Throughout her masters, Grace sought out professional development opportunities that focused on improving learning and teaching in higher education. As ACENET’s Training Coordinator she is responsible for the organization and landscape of ACENET’s Training. Most recently, this has included offering support to increase the training resources available to the Humanities and Social Sciences community.
Lydia Vermeyden joined ACENET in 2020 and is located at St. Francis Xavier University Campus. Lydia works closely with faculty and academic research groups requiring access to the Compute Canada Federation’s national digital infrastructure and resources. She provides consultation and training to help determine the most appropriate digital tools, design data workflows, setup virtual machines in the cloud, get started using high performance computing clusters and advise on storage. Her research background includes clinical psychology (perinatal maternal mental health, e-screening and e-intervention) and public health administration (experiences and opinions around health and safety, and the effects of employment legislation on family farms in Alberta).

Schedule

Day 1

9:00–9:15 am Introduction and Course Plan
9:15–11:00 am Intro to Command Line
11:00–11:15 am Break
11:15–1:00 pm Intro to Python

Day 2

9:00–11:00 am Dive into Python part 1
11:00–11:15 am Break
11:15–1:00 pm Dive into Python part 2

Day 3

9:00–10:15 am Web Scraping
10:15–10:20 am Break
10:20–11:35 am API Querying
11:35–11:45 am Break
11:45–1:00 pm Topic Modelling

Day 4

9 am–1 pm Office hours

Day 5

9:00–11:00 am Project Presentations
11:00–11:15 am Break
11:15–1:00 pm Project Presentations

Project selection

There will be an independent study project as part of the course, for which we will assign small groups (2–3 people depending on shared interests). We have built a google form to help us assign groups and projects. Please fill out the form no later than May 14th as this will allow us to plan for projects and groups ahead of time and tailor content accordingly.

Google form for project selection

If you don't have a project in mind you would like to complete there are several suggestions to choose from on the form:

Web scraping

This is an automated way to pull specific data from webpages. So this could be text, links, images etc., but the key is that this data is kept on web pages in a format and quantity such that it is difficult or time consuming to gather manually. Consider, you want to gather all the public health notices on all public health canadian websites that mention "lockdown". there are going to be several websites with information that is not in an easily exportable form. Perhaps you want to do this repeatedly over time. Python allows you to write a script that can gather this data for you automatically.

API querying

This is similar to web scraping and is also all about gathering data. Some websites (like twitter) have an application programming interface (API) . This is basically a little application over the big application (twitter for example) that can extract data from the big application and respond to commands from programs like python. Lots of social media platforms and many popular websites have APIs that can be used to gather curated datasets. (ex. I want all the tweets in the last month containing "snow in April").

Topic modelling

This is a tool that came out of machine learning, and it uses an algorithm to sort textual data at the individual word level. It treats the textual data like a "bag of words" or "several bags of words" for multiple manuscripts and collects the text words into topics. These topics are defined by a set number of keywords, and gives the researcher an insight into the kinds of words that are clustering together in their textual dataset.

Interactive game 1

Build a text based adventure game based on a theme of your choice.

Interactive game 2

Build an interactive rock-paper-scissors (-lizard–Spock) game.

Interactive game 3

Build a mad libs generator.

Custom notification

Build a desktop notification application which gives you notifications based on your specific criteria.

Software installation (optional)

If you want to use Python on your machine, instead of/in addition to using it on our training cluster, you will need to install the following:

Python and Python packages

The simplest way to install Python and a number of very useful packages and tools is to install Anaconda. Follow the instructions from that link for your operating system.

Terminal

Linux and MacOS users already have a terminal. So this section is for Windows users only.

Windows users should install Git for Windows. While we will not use Git in this course, this software comes with a good Bash emulation called "Git Bash".

JupyterLab

Once you have a terminal and Anaconda installed, you will be able to install JupyterLab. As this installation requires the use of the command line, we will help you with this at the start of the course if you have any issues.

A good text editor

Microsoft Word and other word processors are not text editors: they add a lot of invisible formatting to the text you type. For this course, you need a text editor, so they are not suitable.

Notepad—which comes with Windows—is a text editor, but it is too limited.

Examples of good free text editors suitable for beginners are Visual Studio Code, Atom, Notepad++, Sublime Text. Once you have installed one, it would be a good idea to familiarize yourself with it.

Access to our training JupyterHub

Instructions to access our training cluster.

Resources

There are many books on Python, several of which can be accessed online for free, either directly, or through your university.

Books by O'Reilly

  1. Think Python, 2nd Edition, by Allen B. Downey
  2. Python Pocket Reference, 5th Edition, by Mark Lutz
  3. Introducing Python, by Bill Lubanovic
  4. Python in a Nutshell, 3rd Edition, by Alex Martelli, Anna Ravenscroft, and Steve Holden
  5. Learning Python, 5th Edition, by Mark Lutz
  6. Python Cookbook, 3rd Edition, by David Beazley and Brian K. Jones
  7. The Hitchhiker's Guide to Python, by Kenneth Reitz and Tanya Schlusser
  8. Fluent Python, by Luciano Ramalho
  9. High Performance Python, by Micha Gorelick and Ian Ozsvald
  10. Web Scraping with Python, by Ryan Mitchell
  11. Python Data Science Handbook, by Jake VanderPlas
  12. Python for Data Analysis, by Wes McKinney
  13. Foundations for Analytics with Python, by Clinton W. Brownley
  14. Data Wrangling with Python, by Jacquiline Kazil and Katharine Jarmul
  15. Data Visualization with Python and Javascript, by Kyran Dale
  16. Natural Language Processing with Python, by Steven Bird and Ewan Klein
  17. Thoughtful Machine Learning with Python, by Matthew Kirk
  18. Python for Finance, by Yves Hilpisch

Books by No Starch Press

  1. Automate the Boring Stuff with Python, by Al Sweigart
  2. Python Crash Course, by Eric Matthews
  3. Python Playground, by Mahesh Venkitachalam
  4. Doing Math with Python, by Amit Saha
  5. Invent Your Own Computer Games with Python, by Al Sweigart

Other books

  1. Python Machine Learning, by Sebastian Raschka
  2. Practical Programming: An Introduction to Computer Science Using Python 3, by Paul Gries, Jennifer Campbell, and Jason Montojo
  3. Python for Dummies, by Stef Maruch and Aahz Maruch
  4. Python Essential Reference, 4th Edition, by David Beazley
  5. Head First Python, by Paul Barry
  6. Python for Data Science for Dummies, by John Paul Mueller and Luca Massaron
  7. Beginning Programming with Python for Dummies, by John Paul Mueller
  8. Python for Everybody, by Charles Severance