update and fix typos in readme

seanreed1111 · seanreed1111 · commit 111f8b177a1e · 2019-09-16T08:31:23.000-04:00
diff --git a/README.md b/README.md
@@ -4,24 +4,11 @@
 
 This Jupyter notebook introduces you to some basic principles data exploration and visualization using the Python language. The lessons contained in the notebook, revolve around exploration of the characteristics of a number of automobiles. The ultimate goal is to build a model for predicting the price of a car from its characteristics. The goal is to understand the relationships in these data using visualization methods. 
 
-## About this Jupyter Notebook
-The Jupyter notebook contains material to help you
-
-``pip install seaborn``  
-
 
 
 ## About the author
 
-These lessons have been prepared by **Steve Elston**, co-founder and principle consultant at Quantia Analytics, LLC. Steve is a long time data geek and analytical software pioneer, having lead R&D at the SPlus companies and co-fou learn how to explore data visually. This notebook and the data set can be downloaded from GitHub:
-
-**https://github.com/StephenElston/ExploringDataWithPython**  
-
-This notebook was constructed using the Anaconda 3.5 Python distribution. If you are not running version Anaconda 3.5 or higher, we suggest you update your Anaconda distribution now.  You can download the Python 3 Anaconda distribution for your operating system from the [Continum Analytics web site](https://www.continuum.i
-
-``conda install seaborn``
-
-or nding FinAnalytica, among other positions. He assists clients with staff training and analytics problems in a number of areas. A summary of his activities include:  
+These lessons have been prepared by **Steve Elston**, co-founder and principle consultant at Quantia Analytics, LLC. Steve is a long time data geek and analytical software pioneer, having lead R&D at the SPlus companies and co-founder of FinAnalytica, among other positions. He assists clients with staff training and analytics problems in a number of areas. A summary of his activities include:  
 
 - Instructor, University of Washington and Harvard University data science programs
 - O'Reilly author and creator of four edX data science courses
@@ -30,15 +17,17 @@ or nding FinAnalytica, among other positions. He assists clients with staff trai
 - Experience in several industries: payment, telecom, capital markets, logistics, energy
 - PhD, MS in Geophysics from Princeton University
 
-## Why visualization?
+**https://github.com/StephenElston/ExploringDataWithPython**  
 
-Visualization is an essential method in any data scientist’s toolbox. Visualization is a key first step in the exploration of most data sets. As a general rule, you should never start creating models until you have an understanding of the relationships in your data.
+This notebook was constructed using the Anaconda 3.5 Python distribution. If you are not running version Anaconda 3.5 or higher, we suggest you update your Anaconda distribution now.  You can download the Python 3 Anaconda distribution for your operating system from the [Continum Analytics web site](https://www.continuum.io
 
-To run this notebook you need the Seaborn graphics packages. If you have not done so, you will need to install Seaborn as it is not in the Anaconda distribution as of now. From a command prompt on your computer type the following command. If no errors occur, you will have installed Seaborn.
+Run ``conda install pandas numpy seaborn`` from your terminal or Anaconda Prompt.
 
-``pip install seaborn``
 
-or you have examined the data and understand the relationships. Otherwise, you risk wasting your time creating models blindly. Visualization is also a powerful tool for presentation of results and for determining sources of problems with analytics. 
+
+## Why visualization?
+
+Visualization is an essential method in any data scientist’s toolbox. Visualization is a key first step in the exploration of most data sets. As a general rule, you should never start creating models until you have an understanding of the relationships in your data or you have examined the data and understand the relationships. Otherwise, you risk wasting your time creating models blindly. Visualization is also a powerful tool for presentation of results and for determining sources of problems with analytics. 
 
 The concepts of exploring a data set visually were pioneered by John Tukey in the 1960s and 1970s. Tukey consolidated his many ideas on data exploration into a book in the late 1970s, ***John Tukey, Exploratory Data Analysis, 1977, Addison-Westley***.
 
