Update README.md

README.md

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 # big-data-hackathon-2017
 This repository contains the materials created during the UIowa Big Data Hackathon from September 16th to September 18th.
 
-One of the data visualizations was of the cost per person of a specific procedure for any given state.
+First and foremost, the crux of the project depended on big data representation using Bokeh/Pandas and later embedded onto 
+a webpage using Django and Bootstrap. The specific dataset in question is "Inpatient Prospective Payment System (IPPS) Provider Summary for the Top 100 Diagnosis-Related Groups (DRG)" which is available to the public from data.cms.gov.
+
+Pandas allowed for easy manipulation/viewing of initial changes and formatting of different subsets of the above set.
+Moving to Bokeh was a new experience but allowed the cleanest representation given the time.
+
+The first data table was strictly comparing the number of a specific procedure versus the population of a state for all states.
+This later evolved into an idea of comparing number of procedures per average cost of the procedure in a given area.
+The result of the above would yield an "efficiency" map for states in the united States given an operation of interest.
+
+One of the other data visualizations was of the cost per person of a specific procedure for any given state.
 This data can be further augmented to see which procedures are most cost-efficient in a state vs most-expensive.
 Furthermore, cross-comparing states for most cost-efficient procedures in the United States is also viable.
+
+Challenges faced along the way were that two members had not experienced Python before this day (one of created the Bokeh graphs),
+nobody had formal education on data analysis, and one of the members that did know Python was incapacitated for eight hours over a 
+Numpy and Pandas packaging error that was finally resolved by reinstalling the current Anaconda distribution.