Formatting tips and checklists

In this section, you’ll find additional details to format your submission perfectly and maximize its chances of getting selected

Formatting checklist for written content

If you are submitting written content (blogs, article, etc..) — make sure to go through the following checklist

• Are there any spelling mistakes, errors, grammatical errors, or excessive use of punctuation?

• Are sentences too long? If so, consider breaking them up.

• Organize your articles in a well-structured paragraph

• Use headings correctly. We expect you to use Heading 1 for titles, Heading 2 for subtitles, Heading 4 for sub-sub-titles and so on. Make sure to read our SEO guidelines for more details.

• Use bullet points and numbered lists when applicable to make reading easier

• Hyperlink external links instead of adding them as a source or citing them. For example “According to McKinsey, 56% of executives report AI adoption in their organizations.”

• Use American English (e.g., visualization — not visualisation)

• Oxford comma should be used in all instances in which there are at least three items listed in a series, including in headlines/headers. (e.g., Python, R, and SQL)

• Hyphens should only ever be used to hyphenate compound words or phrases. When using a long dash, ensure that it is in fact a true long dash/em dash (“—”) and that there are no spaces on either side of it.

• Numbers between zero and nine should be written out as shown here, while numbers 10 and above should be displayed in numerals as shown here.

• Generic terms that are not proper nouns (e.g. data science, machine learning, artificial intelligence, data literacy) should never be capitalized.

Formatting checklist for coding tutorials in R and Python using DataCamp Workspace

When crafting tutorials on R and Python—we encourage the use of DataCamp Workspace publication. Publications are great coding environments that are accessible by anyone in the community. Here are some tutorials to get you started:

• Creating a workspace publication

• Installing packages

• Creating a publication

Below, you will also find some notebook and dataset content guidelines that we found generated in the best tutorial experience for readers:

Notebook titles

• Use <h1> or # and title case.

• Keep this under 60 characters.

Notebook titles

• Code cells should only be separated if there is an output to show or a transitional Markdown cell.

• When outputting tables, just use the DataFrame’s name, instead of .head() or .tail()—This enables prettier tables.

• Ensure your code follows black code style.

• Code comments: Always begin with a capital letter. No period punctuation.

Markdown cells

• Use code formatting (i.e., code) for variables, file names, arguments, functions, objects, and True or False boolean values

• Column names and strings should be in double quotes (e.g., "revenue_column").

• Headers that are not the title should be in sentence case and should never use H1. Make sure to follow the logical order of headings (H2, H3, H4..).

• Headers and their associated paragraph text should be in the same Markdown cells. This reduces the number of cells a user may want to delete.

• Workspace doesn’t have spell-check. Make sure to run your text through a spell-checker or Grammarly.

Writing style

Tutorials should avoid the use of the first-person point of view (e.g., "We", "I", "Our", etc.). Instead, the sentence's subject should either be the user of the template or the template/code itself. For example, "The code below plots a…" or "You can set the…". This style makes the template sound more formal and instructional.

Datasets

Whenever possible, always try to use an interesting dataset. What is an interesting dataset?

• Original—generic datasets such as the titanic dataset, iris dataset, are heavily used in tutorials. Try to avoid that when possible.

• Relevant—datasets that tend to be culturally relevant tend to generate the most traction. Examples of such datasets would be F1 championship data, Wordle datasets, and more.

• Interesting—the best datasets tend to be interesting for the author themselves. So ask yourself, is this an interesting dataset to do a tutorial on?

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