diff --git a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
index 8f065ad61cfd1202ff60245a4ff73da2b339e587..bab0d055aa65061b50cb145d62b9a629ed288e6c 100644
--- a/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
+++ b/doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md
@@ -99,9 +99,6 @@ Dask is composed of two parts:
 
 - Dynamic task scheduling optimized for computation and interactive
   computational workloads.
-- Big Data collections like parallel arrays, data frames, and lists
-  that extend common interfaces like NumPy, Pandas, or Python
-  iterators to larger-than-memory or distributed environments. These
 - Big Data collections like parallel arrays, data frames, and lists that extend common interfaces
   like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments.
   These parallel collections run on top of dynamic task schedulers.