This talk is intended for beginner and intermediate data scientists/ analysts/ engineers, although I hope that even experienced data scientists can gain something from the talk.
The talk will focus on using Jupyter notebooks in data science applications. I will discuss the basics of how to get it up and running and the common features like using markup and code in the same notebook, I will highlight the advantages of working in a notebook rather than a traditional IDE. I will also discuss other features like using code snippets, autocomplete, linting and creating a table of contents. Inserting images and videos into a notebook along side your code can be a handy way of learning something new. I will end the talk with a look into jupyterlab.
Attendees of this course will gain an understanding and appreciation of the quick prototyping that is afforded to you when using Jupyter notebooks in your data science pipeline. Especially when it comes to exploratory data analysis. I want to be able to showcase all the common features of Jupyter notebooks but also some less known ones, so that there everyone attending the talk will learn something.