To market your course effectively, you'll need to write a one-paragraph course description that will appear on the course landing page, so learners will see it. It's important to remember that this is for marketing purposes -- you do not want to write an academic syllabus for your course. DataCamp learners are taking courses in their leisure time, and fun courses (and descriptions) are much more appealing. Read on for tips and tricks on writing good course descriptions.

Style Recommendations

  • Aim for 600 characters, with 400 to 900 characters being an acceptable range. Note that this count includes spaces.
  • Your tone should be professional and style should be in line with DataCamp’s style guide.
  • Avoid the use of “In this course/project.”

Sell the course

This paragraph constitutes your sales pitch to learners, 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 they'll learn.

Mention the datasets

Listing the datasets that are used in the course gives a lot of implicit information to learners (i.e. 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 &ndash; for an en-dash.


Intermediate Python for Data Science’s course description is straightforward and concise, explaining why it’s important and what learners will be doing.

  • Intermediate Python for Data Science is crucial for any aspiring data science practitioner learning Python. Learn to visualize real data with Matplotlib's functions and get acquainted with data structures such as the dictionary and the pandas DataFrame. After covering key concepts such as boolean logic, control flow, and loops in Python, you'll be ready to blend together everything you've learned to solve a case study using hacker statistics.

Interactive Data Visualization with Bokeh’s course description explains what Bokeh is and the capabilities it can unlock for learners.

  • Bokeh is an interactive data visualization library for Python — and other languages — that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.
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