diff --git a/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md b/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md
index 05c502a6c..7b653a47c 100644
--- a/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md
+++ b/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md
@@ -45,7 +45,7 @@ User_2: [ 0,  0,  0,  0,  4,  0,  0,  2,  0,  0 ]
 
 Comparing two sparse representations, you'd be usually interested in how much they agree on the same features/objects (e.g., the same movie rating). 
 
-The **dot product** distance metric, introduced in the **day 1** (*Vector Search Fundamentals/Distance Metrics*), is a perfect fit for measuring the similarity between sparse representations. 
+The **dot product** distance metric, introduced in the **Day 1** (*Vector Search Fundamentals/Distance Metrics*), is a perfect fit for measuring the similarity between sparse representations. 
 It multiplies corresponding dimensions and summes the results.
 
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