diff --git a/quickstart/quickstart.md b/quickstart/quickstart.md
index 3bdfa54..e4e29d4 100644
--- a/quickstart/quickstart.md
+++ b/quickstart/quickstart.md
@@ -1,6 +1,15 @@
 ## Quickstart
 This quickstart assumes users have already installed fedeca in a conda environment.  
 
+We recommend users to first install ipython (`pip install ipython`) or jupyter,
+and to copy-paste and run the content of the blocks sequentially either in the
+ipython shell or in a jupyter notebook.  
+
+(Don't forget to make sure the `ipython` interpreter being called is the one from the fedeca
+conda environment by calling `which ipython`. In the case it is not the correct one
+running `hash -r` usually does the trick. Similarly when using ``jupyter`` make sure
+the kernel used is the python interpreter from the conda environment (see i.e. this [stackoverflow question](https://stackoverflow.com/questions/39604271/conda-environments-not-showing-up-in-jupyter-notebook>))) 
+
 FedECA tries to mimic scikit-learn API as much as possible with the constraints
 of distributed learning.
 The first step in data science is always the data.
@@ -9,14 +18,6 @@ Note that fedeca should work on any data format, provided that the
 return type of the substra opener is indeed a pandas.dataframe but let's keep
 it simple in this quickstart.
 
-We recommend users to first install ipython (`pip install ipython`) or jupyter,
-and to copy-paste and run the content of the blocks sequentially either in the
-ipython shell or in a jupyter notebook.
-
-(Don't forget to make sure the `ipython` being called is the one from the fedeca
-conda environment by calling `which ipython`. In case it is not the correct one
-running `hash -r` usually does the trick.)
-
 
 Here we will use fedeca utils which will generate some synthetic survival data
 following CoxPH assumptions: