Some Python and/or Command Line experience
Interest in cloud computing
One of the challenges of writing programs is having the ability to pick up where you left off. Jupyter Notebooks do this very well because they allow for interactive running of code, keep results in memory, and empower data scientists and programmers everywhere to build interesting things.
Jupyter is an interactive notebook which allows you to run code right inside the notebook. This is good for many reasons:
- Notebooks, like actual books, can be shared easily
- While running a notebook, the “state”, is remembered: run some code and it’ remembers the results of that code
- Notebooks easily layout the process of a given program into a story-like progression that anyone can pick up and use.
If you’ve never worked with Jupyter Notebooks before, I highly recommend that you do.
This course is about bringing Jupyter Notebooks to the AWS Cloud. We’ll be preparing a AWS Virtual Private Cloud, Subnets, Internet Gateways, Route Tables, and more so that we can provision AWS EC2 instances securely and effectively.
Once we have an EC2 instance, we can configure nginx, supervisor, and Jupyter so we can run it automatically on a public IP address… check out the first video for more on this.
Who this course is for:
- Data Scientists looking to control their Jupyter notebook experience
- Data Scientists looking for “pick up where you left off” usage of a jupyter notebook
- Pythonistas looking to get into Data Science.