diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md
index 2795874f0..6d3a33c49 100644
--- a/website/docs/api/cli.md
+++ b/website/docs/api/cli.md
@@ -274,7 +274,7 @@ an approximate language-modeling objective. Specifically, we load pre-trained
 vectors, and train a component like a CNN, BiLSTM, etc to predict vectors which
 match the pre-trained ones. The weights are saved to a directory after each
 epoch. You can then pass a path to one of these pre-trained weights files to the
-'spacy train' command.
+`spacy train` command.
 
 This technique may be especially helpful if you have little labelled data.
 However, it's still quite experimental, so your mileage may vary. To load the
diff --git a/website/docs/usage/spacy-101.md b/website/docs/usage/spacy-101.md
index 9aa4061f1..badda4a0b 100644
--- a/website/docs/usage/spacy-101.md
+++ b/website/docs/usage/spacy-101.md
@@ -95,21 +95,6 @@ systems, or to pre-process text for **deep learning**.
   publishing spaCy and other software is called
   [Explosion AI](https://explosion.ai).
 
-<Infobox title="Download the spaCy Cheat Sheet!">
-
-[![spaCy Cheatsheet](../images/cheatsheet.jpg)](http://datacamp-community-prod.s3.amazonaws.com/29aa28bf-570a-4965-8f54-d6a541ae4e06)
-
-For the launch of our
-["Advanced NLP with spaCy"](https://www.datacamp.com/courses/advanced-nlp-with-spacy)
-course on DataCamp we created the first official spaCy cheat sheet! A handy
-two-page reference to the most important concepts and features, from loading
-models and accessing linguistic annotations, to custom pipeline components and
-rule-based matching.
-
-<p><Button to="http://datacamp-community-prod.s3.amazonaws.com/29aa28bf-570a-4965-8f54-d6a541ae4e06" variant="primary">Download</Button></p>
-
-</Infobox>
-
 ## Features {#features}
 
 In the documentation, you'll come across mentions of spaCy's features and
diff --git a/website/meta/universe.json b/website/meta/universe.json
index 41960cec4..29e050964 100644
--- a/website/meta/universe.json
+++ b/website/meta/universe.json
@@ -883,36 +883,6 @@
             "author": "Bhargav Srinivasa-Desikan",
             "category": ["books"]
         },
-        {
-            "type": "education",
-            "id": "datacamp-nlp-fundamentals",
-            "title": "Natural Language Processing Fundamentals in Python",
-            "slogan": "Datacamp, 2017",
-            "description": "In this course, you'll learn Natural Language Processing (NLP) basics, such as how to identify and separate words, how to extract topics in a text, and how to build your own fake news classifier. You'll also learn how to use basic libraries such as NLTK, alongside libraries which utilize deep learning to solve common NLP problems. This course will give you the foundation to process and parse text as you move forward in your Python learning.",
-            "url": "https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python",
-            "thumb": "https://i.imgur.com/0Zks7c0.jpg",
-            "author": "Katharine Jarmul",
-            "author_links": {
-                "twitter": "kjam"
-            },
-            "category": ["courses"]
-        },
-        {
-            "type": "education",
-            "id": "datacamp-advanced-nlp",
-            "title": "Advanced Natural Language Processing with spaCy",
-            "slogan": "Datacamp, 2019",
-            "description": "If you're working with a lot of text, you'll eventually want to know more about it. For example, what's it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other? In this course, you'll learn how to use spaCy, a fast-growing industry standard library for NLP in Python, to build advanced natural language understanding systems, using both rule-based and machine learning approaches.",
-            "url": "https://www.datacamp.com/courses/advanced-nlp-with-spacy",
-            "thumb": "https://i.imgur.com/0Zks7c0.jpg",
-            "author": "Ines Montani",
-            "author_links": {
-                "twitter": "_inesmontani",
-                "github": "ines",
-                "website": "https://ines.io"
-            },
-            "category": ["courses"]
-        },
         {
             "type": "education",
             "id": "learning-path-spacy",