Help & Info

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

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

9:00–11:00am Room 1: Topic Modeling; Room 2: Web Scraping
11:00–11:15am Break
11:15–1:00am Room 1: Natural Language Processing; Room 2: API querying \\

5:30 am -5:30 pm Office hours

5:30 -8:30 am 9:00–11:00am Project Presentations
11:00–11:15am Break
11:15–1:00pm Project Presentations

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.

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").

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.

Natural language processing is a subfield of artificial intelligence focusing on the computational processing of human languages. Its applications are diverse and include language translation, predictive text, message filtering, text analytics, survey analysis, voice assistants, processing of notes…
With the explosion of machine learning techniques such as deep neural networks in recent years, this field has become extremely active, with rapidly growing potential.

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

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

Build a mad libs generator.

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

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.

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".

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.

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.

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