Computational tools for Statistics

Today's presentation focuses on the software that we'll use in this class - both for computation and communication.

Specifically, we'll look at

• How to communicate properly in our forum using Markdown
• Python and its libraries for statistical computation
• How to run Python in a notebook environment that can be annotated with (again) Markdown.

Markup and Markdown

Webpages are generally written in HTML, or HyperText Markup Language. In fact, HTML was one of the first examples of a Markup language but many other's followed.

The idea is awesome - you write text with extra information indicating how you want the text displayed or where to put a picture or a link or a program or all kinds of things.

HTML example

As a very simple example:

Click here! Do it, Do it!

Can be typed:

Click <a href="https://marksmath.org/">here</a>!
<span style="font-style:italic">Do it</span>,
<span style="font-weight:bold">Do it</span>!

Markdown

The problem with Markup is that it can become quite cumbersome. Thus, Markdown came along as a lightweight alternative. For example, the previous "Click here! Do it, Do it!" example can be typed as

Click [here](https://marksmath.org)! *Do it*, **Do it**!

Note that italic and bold text are particularly simple. Generally, you can do more with Markup but the things that can be done with Markdown are relatively simple.

Extensions

Markdown is relatively small but it can be extended. For example, our forum uses an extension of Markdown to enable AsciiMath. That's why %%x^2%% will render as $x^2$, when you type it in the forum.

Other uses

Markdown is used very widely in technical communication. Lot's of forum software like Quora and StackExchange use it; as do a number of website builders, and editors for technical documentation.

Not only does our forum use it but so does our Python environment.

Speaking of the forum, let's go take a look.

Python and Colab

We'll be using a computer language called Python to manipulate, visualize, and do computations with data.

Google provides an awesome environment called Colab that allows you to run Python right in your web browser. Your notebooks will be saved to your Google drive.

To get started on our first lab, just click this link!