# Step 5.1 - Course Description

Write a paragraph describing the course. This will appear on the course landing page, so students will see it.

## Examples

From a course on spatial statistics. This has a good sales pitch.

- Everything happens somewhere, and increasingly the place where all these things happen is being recorded in a database. There is some truth behind the oft-repeated statement that 80% of data have a spatial component. So what can we do with this spatial data? Spatial statistics, of course! Location is an important explanatory variable in so many things - be it a disease outbreak, an animal's choice of habitat, a traffic collision, or a vein of gold in the mountains - that we would be wise to include it whenever possible. This course will start you on your journey of spatial data analysis. You'll learn what classes of statistical problems present themselves with spatial data, and the basic techniques of how to deal with them. You'll see how to look at a mess of dots on a map and bring out meaningful insights.

From a course on ggplot2. This nicely balances motivation and description of contents.

- The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. Here, we’ll examine the first three essential layers for making a plot - Data, Aesthetics and Geometries. By the end of the course you will be able to make complex exploratory plots.

## FAQs

### How long should it be?

Aim for 600 characters, with 400 to 900 characters being an acceptable range.

## Good ideas

### Sell the course

This paragraph constitutes your sales pitch to students, so it needs to provide a reason for them to take the course. This means that you need to talk about *why* the topic is important, as well as *what* the students will learn.

### Mention the datasets

Listing the datasets that are used in the course gives a lot of implicit information to students (are they business-focused/science-focused/whimsical?).

## Common problems and their solutions

### Unbalanced *why* and *what*

If you spend all the time explaining why the students should take the course, it won't be clear what they will be doing. If you spend all your time explaining what they will be doing, they won't know why to take the course. If in doubt, a reasonable balance is often achieved by structuring the description as follows:

- One or two sentence describing why the topic is important.
- Two or three sentences describing what problems the students will solve, or what techniques they will learn.
- One sentence describing the datasets they will encounter.

## How will this be reviewed?

This step will be reviewed by at least two people other than your Curriculum Lead, possibly including an external reviewer. They will look for the following points.

- Is it about the right length?
- Does the course sound exciting?
- Does it explain why the topic is important?
- Does it concisely cover the most important topics/problems/techniques that the course covers?
- If the datasets are interesting, does it mention them?