During COVID-19, Research Commons' services continue.
SFU graduate students are encouraged to book consultations with the Research Commons staff and partners. Consultations are available by phone, via email, or through online video-conference.
Not finding what you're looking for? Please get in touch with us at firstname.lastname@example.org so we can discuss your research support needs.
This workshop is in the past and registrations are unavailable.
All times are Pacific Time Zone (Vancouver, BC, Canada).
A Jupyter notebook is an application which can be used to produce interactive computational narratives containing code (e.g., R, Python, Julia) and formatted text (e.g., paragraphs, formulas). Jupyter notebooks are a powerful open source tool for scientific computing and can be used to clean, visualize and analyze data as well as showcase models and simulations, while also facilitating sharing results with the scientific community.
By the end of this workshop you will be familiar with the Jupyter environment and will be able to define, test and run functions, run a Python-based simulation and create an interactive narrative which can be shared with colleagues.
Setup & Software Installation
Participants will need to install the following on their device.
We will be using Python 3.7 and Jupyter notebooks throughout the workshop. The easiest way to install is via Anaconda https://docs.anaconda.com/anaconda/install/. We will also explore incorporating an IDE such as PyCharm https://www.jetbrains.com/pycharm/, please ensure you install this.
In addition to this, please make sure you install the following Python dependencies:
Matplotlib (Instructions https://matplotlib.org/3.1.1/users/installing.html )
Numpy (Instructions https://scipy.org/install.html )
Pandas (Instructions https://pandas.pydata.org/pandas-docs/stable/install.html)
Plotly (Instructions https://pypi.org/project/plotly/)
You need to attend BOTH days. Different topics are covered each day and it builds on materials covered on the previous day, so if you miss a day, we won't have the resources to help you catch up in this online environment. To give you an idea of what's being covered each day, here is a very rough draft of the topics being covered each day. They will be refined each day as required.
Day 1 (June 8): Recap of basic and intermediate Python programming. Jupyter notebook essentials
Day 2 (June 9): Answering a data science question using Jupyter notebooks / Discussion / Q & A