diff --git a/episodes/01-getting-started.md b/episodes/01-getting-started.md
index f853a6b0..ddf8db04 100644
--- a/episodes/01-getting-started.md
+++ b/episodes/01-getting-started.md
@@ -26,7 +26,7 @@ exercises: 0
 - Data is often very messy, and this tool saves a lot of time on cleaning
   headaches.
 
-- Data cleaning steps often need repeating with multiple files. It is important to know what you did to your data. This makes it easy for you to repeat these steps again with similarly structured data. OpenRefine is
+- Data cleaning steps often need repeating with multiple files. It is important to know what you did to your data. This makes it possible for you to repeat these steps again with similarly structured data. OpenRefine is
   perfect for speeding up repetitive tasks by replaying previous actions on
   multiple datasets.
 
@@ -35,11 +35,11 @@ exercises: 0
   all actions applied to your raw data and share them with your publication as
   supplemental material.
 
-- Any operation that changes the data in OpenRefine can be easily reversed or
+- Any operation that changes the data in OpenRefine can be reversed or
   undone.
 
-- Some concepts such as clustering algorithms are quite complex, but OpenRefine
-  makes it easy to introduce them, use them, and show their power.
+- Some concepts such as clustering algorithms are quite complex, but with OpenRefine
+  we can introduce them, use them, and show their power.
   
   > **Note:** You must export your modified dataset to a new file: OpenRefine does not save over the original source file. All changes are stored in the OpenRefine project.
 
@@ -50,7 +50,7 @@ The following setup is necessary before we can get started (see the [instruction
 ## What is OpenRefine?
 
 - OpenRefine is a Java program that runs on your machine (not in the cloud): it is a desktop application that uses your web browser as a graphical interface. No internet connection is needed, and none of the data or commands you enter in OpenRefine are sent to a remote server.
-- OpenRefine does not modify your original dataset. All actions are easily reversed in OpenRefine and you can capture all the actions applied to your data and share this documentation with your publication as supplemental material.
+- OpenRefine does not modify your original dataset. All actions can be reversed in OpenRefine and you can capture all the actions applied to your data and share this documentation with your publication as supplemental material.
 - OpenRefine saves as you go. You can return to the project at any time to pick up where you left off or export your data to a new file.
 - OpenRefine can be used to standardise and clean data across your file.
 
diff --git a/episodes/02-importing-data.md b/episodes/02-importing-data.md
index b19b4fb3..7d373a0e 100644
--- a/episodes/02-importing-data.md
+++ b/episodes/02-importing-data.md
@@ -74,7 +74,7 @@ The columns are all imported as text, even the columns with numbers. We will see
 
 Once your data is imported into a project - OpenRefine leaves your raw data intact and works on a copy which it creates
 inside the newly created project. All the data transformation and cleaning steps you apply will be performed on this copy
-and you can easily undo any changes too.
+and you can undo any changes too.
 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/episodes/04-transforming-data.md b/episodes/04-transforming-data.md
index 05ffe527..63252886 100644
--- a/episodes/04-transforming-data.md
+++ b/episodes/04-transforming-data.md
@@ -21,7 +21,7 @@ exercises: 15
 
 ## Data splitting
 
-It is easy to split data from one column into multiple columns if the parts are separated by a common separator (say a comma, or a space).
+We can split data from one column into multiple columns if the parts are separated by a common separator (say a comma, or a space).
 
 1. Let us suppose we want to split the `scientificName` column into separate columns, one for genus and one for species.
 2. Click the down arrow next to the `scientificName` column. Choose `Edit Column` > `Split into several columns...`
@@ -58,7 +58,7 @@ Both new columns will appear with green text, indicating they are numeric. The o
 
 ## Undoing / Redoing actions
 
-It is common while exploring and cleaning a dataset to make a mistake or decide to change the order of the process you wish to conduct. OpenRefine provides `Undo` and `Redo` operations to make it easy to roll back your changes.
+It is common while exploring and cleaning a dataset to make a mistake or decide to change the order of the process you wish to conduct. OpenRefine provides `Undo` and `Redo` operations to roll back your changes.
 
 1. Click `Undo / Redo` in the left side of the screen. All the changes you have made will appear in the left-hand panel.
   The current stage in the data processing is highlighted in blue (i.e. step 4. in the screenshot below). As you click