few minor changes to README

README.md

@@ -4,17 +4,18 @@
 
 *All views expressed on this site are my own and do not represent the opinions of any entity with which I have been, am now, or will be affiliated.*
 
-This repository contains all materials related to a lecture / seminar I teach on practical data visualization with python. What I mean by "practical" is that the materials herein do not focus on one particularly library or data visualization method; rather, my goal is to empower the consumer of this content with the tools, heuristics, and methods needed to handle a wide variety of data visualization problems. 
+This repository contains all materials related to a lecture / seminar I teach on practical data visualization with python. What I mean by "practical" is that the materials herein do not focus on one particular library or data visualization method; rather, my goal is to empower the consumer of this content with the tools, heuristics, and methods needed to handle a wide variety of data visualization problems. 
 
-This is a work in progress that will be evolving rapidly over the coming weeks and months, so please check back often for new additions and refinements, and if you'd like to contact me, don't hesitate to reach out [via Twitter here](https://twitter.com/ByPaulJ).
+If you have questions, comments, or suggested alterations to these materials, please [open an issue](https://github.com/pmaji/practical-python-data-viz-guide/issues) here on GitHub. Also, don't hesitate to reach out [via Twitter here](https://twitter.com/ByPaulJ).
 
 ## Outline of Materials
 
-In the section below you'll find a brief outline of the content contained in the four sections of this seminar. For each section there is a separate Jupyter notebook of python code containing all the materials for that section. Each notebook will start with a few setup steps--package imports and data prep mostly--that are almost identical between the notebooks, directly after which comes the content for each section.
+Below you'll find a brief outline of the content contained in the four sections of this seminar. For each section there is a separate notebook of python code containing all the materials for that section. Each notebook will start with a few setup steps--package imports and data prep mostly--that are almost identical between the notebooks, directly after which comes the content for each section. For information about the data used in these materials, check out the `data_prep_nb.ipynb` notebook, the easy-to-view version of which is [hosted here](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/data_prep_nb.ipynb).
 
 ### Why We Visualize
 
 [Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_1_main_nb.ipynb) to the easy-to-view notebook for this section of material.
+<br>
 [Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_1_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.
 
 1. The power of visual data representation and storytelling. 
@@ -24,6 +25,7 @@ In the section below you'll find a brief outline of the content contained in the
 ### Overview of Python Visualization Landscape
 
 [Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_2_main_nb.ipynb) to the easy-to-view notebook for this section of material.
+<br>
 [Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_2_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.
 
 1. Intro to the visualization ecosystem: python's Tower of Babel.
@@ -33,6 +35,7 @@ In the section below you'll find a brief outline of the content contained in the
 ### Statistical Visualization in the Wild
 
 [Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_3_main_nb.ipynb) to the easy-to-view notebook for this section of material.
+<br>
 [Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_3_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.
 
 1. Example business use case of data visualization:
@@ -46,6 +49,7 @@ In the section below you'll find a brief outline of the content contained in the
 ### Library Deep-Dive (Plotly)
 
 [Here is the link](https://nbviewer.jupyter.org/github/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_4_main_nb.ipynb) to the easy-to-view notebook for this section of material.
+<br>
 [Here is the link](https://github.com/pmaji/practical-python-data-viz-guide/blob/master/notebooks/part_4_main_nb.ipynb) to the GitHub-hosted notebook for this section of the material.
 
 1. Quick and simple data visualizations with Plotly Express.
@@ -60,11 +64,15 @@ There is a homework exercise associated with these materials, for those interest
 
 ## Setup Instructions
 
-- clone this repository locally
+- clone this repository
 - create a virtual environment using `python3 -m venv env`
     - additional information about this [can be found here](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)
 - activate that virtual environment using `source env/bin/activate`
 - install needed packages using `pip install -r requirements.txt`
     - additional information about this [can be found here](https://pip.pypa.io/en/latest/user_guide/#requirements-files)
 - run an instance of jupyter lab out of your virutal env using `env/bin/jupyter-lab`
-- start by opening and running the `main_lecture_nb.ipynb` file, in which the majority of the content is located
+- opening and run the four main files of content for this course--one for each section:
+    - `part_1_main_nb.ipynb`
+    - `part_2_main_nb.ipynb`
+    - `part_3_main_nb.ipynb`
+    - `part_4_main_nb.ipynb`