To market your course effectively, you will need to write a one paragraph course description that will appear on the course landing page, so students will see it. It is important to remember that this is for marketing purposes - you do not want to write an academic syllabus for your course. DataCamp students are taking courses in their leisure time, and fun courses (and descriptions) are much more alluring. Read on for tips and tricks on writing a good course description.
How long should it be?
Aim for 600 characters, with 400 to 900 characters being an acceptable range. Note that this count INCLUDES spaces.
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?).
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 sentences describing why the topic is important.
- Two or three sentences describing the problems students will solve, or what techniques they will learn.
- One sentence describing the datasets they will encounter.
Use simple HTML formatting
You can use a limited set of HTML tags to format your text, for example
<b> for bold text and
<em> for emphasized text (italics). You can also use HTML entities for punctuation, for example
– for an en-dash and
& for an ampersand
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 analyzing survey data. This nicely balances motivation and description of contents.
- You've taken a survey (or 1000) before, right? Have you ever wondered what goes into designing a survey and how survey responses are turned into actionable insights? Of course you have! In Analyzing Survey Data in R, you will work with surveys from A to Z, starting with common survey design structures, such as clustering and stratification, and will continue through to visualizing and analyzing survey results. You will model survey data from the National Health and Nutrition Examination Survey using R's survey and tidyverse packages. Following the course, you will be able to successfully interpret survey results and finally find the answers to life's burning questions!