acdick
diff --git a/‎README.md
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diff --git a/‎Data/NYC_Dog_Licensing_Dataset.csv ‎dat/NYC_Dog_Licensing_Dataset.csv b/‎Data/NYC_Dog_Licensing_Dataset.csv ‎dat/NYC_Dog_Licensing_Dataset.csv
diff --git a/‎Data/neighborhoods.csv ‎dat/neighborhoods.csv b/‎Data/neighborhoods.csv ‎dat/neighborhoods.csv
diff --git a/‎Data/pet_care_industry.db ‎dat/pet_care_industry.db b/‎Data/pet_care_industry.db ‎dat/pet_care_industry.db
diff --git a/‎img/01_First_10_Neighborhoods_Mixed_Format.png
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diff --git a/‎img/02_First_10_Neighborhoods_Long_Format.png
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diff --git a/‎img/03_First_10_Pet_Stores.png
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diff --git a/‎img/04_Concentration_Ratios_for_Top_8_Firms.png
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diff --git a/‎img/05_Concentration_Ratios_for_Top_8_Neighborhoods.png
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diff --git a/‎img/06_First_10_Licensed_Dogs.png
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diff --git a/‎img/07_Market_Share_and_Growth_Rate.png
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diff --git a/‎Source/01_Neighborhoods.ipynb ‎src/01_Neighborhoods.ipynb b/‎Source/01_Neighborhoods.ipynb ‎src/01_Neighborhoods.ipynb
diff --git a/‎Source/02_Pet_Stores_and_Services.ipynb ‎src/02_Pet_Stores_and_Services.ipynb b/‎Source/02_Pet_Stores_and_Services.ipynb ‎src/02_Pet_Stores_and_Services.ipynb
diff --git a/‎Source/03_Dogs.ipynb ‎src/03_Dogs.ipynb b/‎Source/03_Dogs.ipynb ‎src/03_Dogs.ipynb
diff --git a/‎Source/04_Exploratory_Data_Analysis.ipynb ‎src/04_Exploratory_Data_Analysis.ipynb b/‎Source/04_Exploratory_Data_Analysis.ipynb ‎src/04_Exploratory_Data_Analysis.ipynb
@@ -1,4 +1,27 @@
+EXPLORATORY DATA ANALYSIS
 # Framing Data with DataFrames
-Channeling Open Data into Business Frameworks
+CHANNELING OPEN DATA INTO BUSINESS FRAMEWORKS
 
-https://towardsdatascience.com/framing-data-with-dataframes-d9b7ce012be5
+At the outset of any challenge, but especially for the complex and ambiguous ones that data scientists often face, structuring a thought process to approach the fundamental issue is one of the initial tasks. Many industries employ subject matter experts that have domain-specific knowledge, where unique paradigms of thinking are continually developed, taught and enhanced.
+
+A functional organizational structure separates employees with specific skills into departments such as Human Resources, Sales, Marketing, Finance and Engineering, etc. This can produce a silo effect, in which useful information is created, but not necessarily shared. In response, companies assemble cross-functional teams to facilitate sharing and learning across the organization.
+
+These teams can increase overall company effectiveness because they strengthen the firm’s interdependent internal network. For example, strategic decisions can be improved by incorporating, say, information and insights inferred by data scientists. So let’s take a look at how rapidly growing data can be channeled into intuitive frameworks that are familiar to business leaders.
+
+[Continue reading the full story on Towards Data Science, a Medium publication](https://towardsdatascience.com/framing-data-with-dataframes-d9b7ce012be5?source=friends_link&sk=0866bef9af6443371ada8b3ed6753e67)
+
+## Project Features
+
+<iframe width="900" height="800" frameborder="0" scrolling="no" src="//plot.ly/~adam.c.dick/2.embed"></iframe>
+
+## Data Products
+
+## Output Results
+
+![First 10 Neighborhoods Mixed Format](01_First_10_Neighborhoods_Mixed_Format.png)
+![First 10 Neighborhoods Long Format](02_First_10_Neighborhoods_Long_Format.png)
+![First 10 Pet Stores](03_First_10_Pet_Stores.png)
+![Concentration Ratios for Top 8 Firms](04_Concentration_Ratios_for_Top_8_Firms.png)
+![Concentration Ratios for Top 8 Neighborhoods](05_Concentration_Ratios_for_Top_8_Neighborhoods.png)
+![First 10 Licensed Dogs](06_First_10_Licensed_Dogs.png)
+![Market Share and Growth Rate](07_Market_Share_and_Growth_Rate.png)