Minor fixes

docs/mooc/parallel-programming/python-in-parallel.md

@@ -2,22 +2,22 @@
 
 <!-- Short description:
 
-In this article we give overview of parallel programming utilities in Python
-ecosystem.
+In this article we give an overview of parallel programming approaches in the
+Python ecosystem.
 
 -->
 
-Python has rich ecosystem also for parallel computing, both standard
+Python has a rich ecosystem also for parallel computing, both standard
 library and third party packages provide tools for different parallel
-programming approaches. 
+programming approaches.
 
-In this course we focus on message passing approach with the mpi4py
-package, which is normally the most appropriate solution for tightly
+In this course we focus on the message passing approach (with the mpi4py
+package), that is normally the most appropriate solution for tightly
 coupled parallel problems. In this article we review briefly some
 other parallel programming packages available for Python, which can be
 useful if the parallel problem is not communication intensive.
 
-Python [standard library](https://docs.python.org/3/library/concurrency.html) 
+Python [standard library](https://docs.python.org/3/library/concurrency.html)
 contains some modules that can be used for parallel programming for a
 single shared memory computer, *i.e.* they are not suitable for
 distributed computing. They are also mainly meant for launching
@@ -26,7 +26,7 @@ needs between the tasks. The
 [threading](https://docs.python.org/3/library/threading.html) module
 provides a thread based approach, whereas
 [multiprocessing](https://docs.python.org/3/library/multiprocessing.html)
-is based on processes. There is also a higher level
+is based on child processes. There is also a higher level
 [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html)
 module which can utilize either threads or processes. Using multiple
 processes has a higher memory overhead, however, in the standard
@@ -35,15 +35,15 @@ Lock, which allows only one thread to execute Python code at once
 (even though certain performance-oriented libraries might overcome
 this limitation). Even though CPU-intensive tasks cannot execute in
 parallel with threading, it can still be an appropriate model if you
-want to run multiple I/O-bound tasks simultaneously. 
+want to run multiple I/O-bound tasks simultaneously.
 
 For parallel data analytics that needs to scale over multiple
 computers, one can utilize [Dask](https://dask.org/) or
 [PySpark](https://spark.apache.org/docs/latest/api/python/pyspark.html)
-packages third party packages. Dask is a Python library providing
+third party packages. Dask is a Python library providing
 advanced parallelism for analytics proving interfaces similar to
 e.g. NumPy. **PySpark** is a Python interface to Apache Spark, a
-general distributed cluster-computing framework for big data processing. 
+general distributed cluster-computing framework for big data processing.
 
 We will now move on to learn message passing with Python in more
 detail, but please comment if you have experiences about these other