**These are the slides, notes, and the resulting video from a presentation I gave at TekMountain on Tuesday, September 17th, inspired by the article mentioned below**

I read this great article just a few weeks ago called “Jupyter is the New Excel“. I loved it, and was provoked by its premise, and wrote to the author to tell her so. This dominant and for many users *intimidating* part of the data science toolchain, called a Jupyter notebook, could be used for more everyday tasks. You didn’t have to do data science *per se* with notebooks, didn’t have to, like, crunch big data, worry about data storage, care what generalized least squares were. Jupyter was easy and useful enough to use for front office tasks, for fantasy football, dinner party invites, what have you. You could fool around with it!

I work at IBM, where I fool around with data science as a rank amateur, and I took the article as a jumping off point: Yes, *how*? *How* would Jupyter replace Excel exactly? *How* could you use Jupyter for your email marketing and fantasy football, for your real estate office spreadsheets?

So I thought I’d create this tech talk, a presentation where we can step through examples of everyday data crunching, the things that many of do now in Excel, see if the article’s premise checks out.

My goal is to introduce Jupyter notebooks very, very briefly, get right into just a few super-practical, everyday tasks, to *not* talk about data science, to welcome and un-intimidate. If I’m successful, I’ll persuade you that Jupyter notebooks are no more complicated to use than Excel, might work better for some things, can be the kind of ready-to-hand tool that spreadsheets are for many now. This might even be a gateway drug to do some better integrations of your data, which is where Jupyter starts to really outpace all these separate, versioned, weirdo macro-laden spreadsheets, or even be a useful starting point for some data-sciencing on your own :-)

## Resources for learning more¶

Jupyter and Python and a lot of the premier data science tools are open source, which means there are a TON of resources out there for learning, trying. Here are a few good ones, focusing again not on the ocean of data science but on Jupyter notebooks:

- jupyter.org/try – Try Jupyter notebooks on a test server
- Install Jupyter – Install Jupyter on your system as part of Anaconda, a really nice and easy-to-use distribution of data science tools
- “Jupyter is the New Excel”, by Semi Koen
- Jupyter Notebook basics – The main tutorial and description of Jupyter notebooks, from readthedocs.io
- iPython – A lot of the interactive and in-cell features of notebooks are in fact iPython
- Python Tutorial (Learn Python)
- Markdown Cheatsheet, a quick reference for Markdown, the simple text formatting in Jupyter notebooks and elsewhere
- The New Excel.ipynb, the notebook that generated the presentation
- The data files referenced in the notebook