Fixed typos

_episodes/01-conda.md

@@ -30,7 +30,8 @@ to make the types of analysis we do faster and more efficient.
 To learn more about the PyAOS "stack" shown in the diagram below
 (i.e. the collection of libraries that are typically used for
 data analysis and visualisation in the atmosphere and ocean sciences),
-check out [this post](https://drclimate.wordpress.com/2016/10/04/the-weatherclimate-python-stack/).
+check out [PyAOS stack](https://carpentrieslab.github.io/python-aos-lesson/stack/index.html)
+page in the extras tab of the menu above.
 
 ![PyAOS stack](../fig/01-pyaos-stack.png) 
 

_extras/stack.md

@@ -4,7 +4,7 @@ title: PyAOS stack
 ---
 
 It would be an understatement to say that Python has exploded onto the data science scene in recent years.
-PyCon and SciPy conferences are held somewhere in the world every few months now,
+PyCon and SciPy conferences are held somewhere around the world every few months,
 at which loads of new and/or improved data science libraries are showcased to the community 
 (check out [pyvideo.org](pyvideo.org) for conference recordings).
 The ongoing rapid development of new libraries means that data scientists are (hopefully)
@@ -19,7 +19,7 @@ The focus is on libraries that are widely used and that have good (and likely lo
 
 ## Core
 
-The dashed box in the diagram represents the core of the stack, so let’s start this tour there.
+The dashed box in the diagram represents the core of the stack, so let’s start our tour there.
 The default library for dealing with numerical arrays in Python is [NumPy](http://www.numpy.org/).
 It has a bunch of built in functions for reading and writing common data formats like .csv,
 but if your data is stored in netCDF format then the default library for getting data
@@ -35,7 +35,7 @@ the [SciPy](https://www.scipy.org/scipylib/index.html) library is the default.
 If you’re dealing with a particularly large dataset,
 you may get memory errors (and/or slow performance)
 when trying to read and process your data.
-[Dask[(https://dask.org/) works with the existing Python ecosystem (i.e. NumPy, SciPy etc)
+[Dask](https://dask.org/) works with the existing Python ecosystem (i.e. NumPy, SciPy etc)
 to scale your analysis to multi-core machines and/or distributed clusters
 (i.e. parallel processing).
 
@@ -117,7 +117,7 @@ The VCDAT application also now runs as a JupyterLab extension, which is an excit
 > ## How to choose
 >
 > In terms of choosing between xarray and Iris,
-> some people like the slightly more atmosphere/ocean-centric experience offered by Iris,
+> some people like the slightly more AOS-centric experience offered by Iris,
 > while others don’t like the restrictions that places on their work
 > and prefer the generic xarray experience
 > (e.g. to use Iris your netCDF data files have to be CF compliant or close to it).