EEB125 Week 1: Computation, Python, and JupyterHub#
Quick Facts#
Name: Karen Reid
Professor, Teaching Stream in the Department of Computer Science
I primarily teach systems programming and operating systems. I.e. Low-level, how does the computer really work kind of stuff.
What is programming?#
A computer program is a set of instructions for a computer to execute.
Just as humans have languages like English to communicate with each other, a programming language is a language that allows humans to communicate these instructions to a computer.
Python!#
In this course, we’ll be using the Python programming language.
Why Python?#
Python is…
beginner friendly (code looks a lot like English)
lots of built-in functionality
powerful data science and data visualization libraries (e.g.
numpy,pandas,plotly)very commonly used in both academia and industry
Writing Python code#
Next week, we’ll start going over the basics of Python programming, but for now, think of Python code as a sequence of very precise instructions that your computer can understand.
my_name = "Karen"
my_students = 400 + 300
print(f"Wow {my_name}, you're teaching {my_students} students. That's a big class!")
my_students = my_students + 120 + 85
print(f"Actually {my_name}, it's more like {my_students}")
Notebooks!#
Traditionally, Python code was written in .py files, called Python modules.
Notebooks!#
In this course, we’ll use Jupyter notebooks to write Python code.
Notebooks let us mix:
Python code
Output of running the code
Text (including nice formatting)
Images
…and more!
Notebooks!#
Fun fact: Jupyter, the software that lets us create notebooks, is written in Python!
Do I need to download and install Python and Jupyter?#
No software installation required!#
In this course, we’ll be using JupyterHub (https://jupyter.utoronto.ca), which runs Python and Jupyter for us online.
You can login to JupyterHub from anywhere with an Internet collection
All of your files are stored “in the cloud”
Runs Python code “in the cloud”
JupyterHub Demo!#
Two ways of accessing JupyterHub:
Go to any notebook on our course website (https://uoftcompdsci.github.io/eeb125-20251/) and click on the little rocket icon:
Example: Let’s check your intuition#
What do you think the following code will do?
What types of data are we working with?
What is the most mysterious thing about the code right now?
What seems obvious?
sentence = "Programming in EEB125 will be fun."
words = sentence.split()
sum = 0
for word in words:
sum = sum + len(word)
average = sum/len(words)
print(f"Average word length is {average}")
Learning and Learning to Program. Some things to remember#
Programming languages are actually for people, not for computers :)
Learning a new programming language is similar to learning a new (written) human language, but … easier
Vocabulary is much, much smaller
Syntax/Grammar are much simpler
The main limitation is that the computer is more strict
The real barrier is understanding what you want to do and explaining that in a clear non self-contradictory way
Learning to read code is perhaps more important that learning to write code:
Data Science tasks rarely start from a blank page
You use, assimilate, and adapt the code and ideas of others
Quite often, you are in a “menu in an exotic restaurant” situation, and that’s okay!
Some Active (Self-)Learning Strategies#
Activating Prior Knowledge
Distributing Practice (not cramming!) and Interleaving (not creating interruptions!)
Elaborative rehearsal: rephrasing, finding examples, integrating with other constructs, organizing in a meaningful way
From strategies to tactics#
Reading your own code and the code of others, highlight and annotate it (notebooks are perfect for that)
Use markdown blocks to write down topic sentences and paragraphs,
Always assume you will need to reread your code and that you will you remember nothing
Dealing with the complex blocks of code you don’t understand just yet, try to write down high-level explanations in plain language
what does it do?
what does it take as an input and produce as an output?
Distributed practice and elaborative rehearsal:
Read the lab and homework at the beginning of the week. If you have time, make comments
Look at the previous homework to find useful chunks
Regularly repeat main commands, functions, and constructs in context
For example, modify a bit chunks from previous homework (What if I change 5 to 6, a number to a character, add another variable?)
Make notes on connections between terminology and Python syntax, between different ways of writing the same code