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 38d198969801a913287d92ffc300b0447bfacddb..4c17864f7ca44f861f2150a31987cfbc85fefa2c 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
@@ -18,9 +18,9 @@ a research group and/or teaching class. For this purpose,
 The interactive Python interpreter can also be used on ZIH systems via an interactive job:
 
 ```console
-marie@login$ srun --partition=haswell --gres=gpu:1 --ntasks=1 --cpus-per-task=7 --pty --mem-per-cpu=8000 bash
-marie@haswell$ module load Python
-marie@haswell$ python
+marie@login$ srun --gres=gpu:1 --ntasks=1 --cpus-per-task=7 --pty --mem-per-cpu=8000 bash
+marie@compute$ module load Python
+marie@compute$ python
 Python 3.8.6 (default, Feb 17 2021, 11:48:51)
 [GCC 10.2.0] on linux
 Type "help", "copyright", "credits" or "license" for more information.
@@ -50,7 +50,7 @@ threads that can be used in parallel depends on the number of cores (parameter `
 within the Slurm request, e.g.
 
 ```console
-marie@login$ srun --partition=haswell --cpus-per-task=4 --mem=2G --hint=nomultithread --pty --time=8:00:00 bash
+marie@login$ srun --cpus-per-task=4 --mem=2G --hint=nomultithread --pty --time=8:00:00 bash
 ```
 
 The above request allows to use 4 parallel threads.
@@ -239,7 +239,7 @@ from distributed import Client
 from dask_jobqueue import SLURMCluster
 from dask import delayed
 
-cluster = SLURMCluster(queue='alpha',
+cluster = SLURMCluster(
   cores=8,
   processes=2,
   project='p_number_crunch',
@@ -294,7 +294,7 @@ for the Monte-Carlo estimation of Pi.
 
     #create a Slurm cluster, please specify your project
 
-    cluster = SLURMCluster(queue='alpha', cores=2, project='p_number_crunch', memory="8GB", walltime="00:30:00", extra=['--resources gpu=1'], scheduler_options={"dashboard_address": f":{portdash}"})
+    cluster = SLURMCluster(cores=2, project='p_number_crunch', memory="8GB", walltime="00:30:00", extra=['--resources gpu=1'], scheduler_options={"dashboard_address": f":{portdash}"})
 
     #submit the job to the scheduler with the number of nodes (here 2) requested:
 
@@ -439,7 +439,6 @@ For the multi-node case, use a script similar to this:
 ```bash
 #!/bin/bash
 #SBATCH --nodes=2
-#SBATCH --partition=ml
 #SBATCH --tasks-per-node=2
 #SBATCH --cpus-per-task=1