Fix hyphenation for high/low{-| }level (#232)

Author: hendrikmakait

00_overview.ipynb

@@ -35,11 +35,11 @@
     "\n",
     "We can think of Dask at a high and a low level\n",
     "\n",
-    "*  **High level collections:**  Dask provides high-level Array, Bag, and DataFrame\n",
+    "*  **High-level collections:**  Dask provides high-level Array, Bag, and DataFrame\n",
     "   collections that mimic NumPy, lists, and Pandas but can operate in parallel on\n",
     "   datasets that don't fit into memory.  Dask's high-level collections are\n",
     "   alternatives to NumPy and Pandas for large datasets.\n",
-    "*  **Low Level schedulers:** Dask provides dynamic task schedulers that\n",
+    "*  **Low-level schedulers:** Dask provides dynamic task schedulers that\n",
     "   execute task graphs in parallel.  These execution engines power the\n",
     "   high-level collections mentioned above but can also power custom,\n",
     "   user-defined workloads.  These schedulers are low-latency (around 1ms) and\n",

02_bag.ipynb

@@ -25,7 +25,7 @@
     "\n",
     "These core data structures are optimized for general-purpose storage and processing.  Adding streaming computation with iterators/generator expressions or libraries like `itertools` or [`toolz`](https://toolz.readthedocs.io/en/latest/) let us process large volumes in a small space.  If we combine this with parallel processing then we can churn through a fair amount of data.\n",
     "\n",
-    "Dask.bag is a high level Dask collection to automate common workloads of this form.  In a nutshell\n",
+    "Dask.bag is a high-level Dask collection to automate common workloads of this form.  In a nutshell\n",
     "\n",
     "    dask.bag = map, filter, toolz + parallel execution\n",
     "    \n",

README.md

@@ -10,11 +10,11 @@ Dask provides multi-core execution on larger-than-memory datasets.
 
 We can think of dask at a high and a low level
 
-*  **High level collections:**  Dask provides high-level Array, Bag, and DataFrame
+*  **High-level collections:**  Dask provides high-level Array, Bag, and DataFrame
    collections that mimic NumPy, lists, and Pandas but can operate in parallel on
    datasets that don't fit into main memory.  Dask's high-level collections are
    alternatives to NumPy and Pandas for large datasets.
-*  **Low Level schedulers:** Dask provides dynamic task schedulers that
+*  **Low-level schedulers:** Dask provides dynamic task schedulers that
    execute task graphs in parallel.  These execution engines power the
    high-level collections mentioned above but can also power custom,
    user-defined workloads.  These schedulers are low-latency (around 1ms) and