updated Readme

EducationAnalytics/README.md

@@ -1,7 +1,7 @@
-# Data Science Design Pattern for Education Analytics
+# Data Science Accelerator for Education Analytics
 
 ## Introduction 
-This Data Science Design Pattern provides a starting point for the data scientist exploring a new dataset in the education world by using R. 
+This Data Science Accelerator provides a starting point for the data scientist exploring a new dataset in the education world by using R. 
 
 Education is a relatively late adopter of predictive analytics or machine learning as a management tool. Nowadays, a desire for stronger operations is leading universities or government to perform student predictive analysis, which helps in better-informed and faster decision making.
 Student predictive analytics aims to solve two key problems:
@@ -11,11 +11,11 @@ Student predictive analytics aims to solve two key problems:
 
 Education systems face an enormous diversity across regions and countries. The case studies in Asia show a novel and unique landscape for machine learning in the education world. 
 
-The current version of this Design Pattern covers the above two typical use cases: 
+The current version of this Accelerator covers the above two typical use cases: 
 - **Student Drop-out Prediction** In this use case, we leverage a series of ML models, two-class classification, to predict the likelihood of a student dropping out for an India State Government. 
 - **Student Score Modeling** In this use case, we build a mixed effects regression model to measure the influence of student characteristics and predict students’ NAPLAN test scores in the presence of variation in students and schools for an Australian Education Department.
 
-By no means is it the endpoint of the data science journey. The pattern is under regular revision and improvement and is provided as is.
+By no means is it the endpoint of the data science journey. The accelerator is under regular revision and improvement and is provided as is.
 
 ## Getting Started
 To get the code up and running on your own system, you should make sure:
@@ -23,13 +23,13 @@ To get the code up and running on your own system, you should make sure:
 - R version 3.2 and 3.3 are needed.
 
 ## Directory
-The following is the directory structure for this design pattern:
+The following is the directory structure for this accelerator:
 - **Data**    This contains the provided sample data. 
 
   - *studentDropIndia*  This sample dataset is simulated from student data in [UCI repository] (https://archive.ics.uci.edu/ml/datasets/Student+Performance#) and an India State Government. 
   - *studentScoreAUS*  This sample dataset is simulated from student data in [UCI repository] (https://archive.ics.uci.edu/ml/datasets/Student+Performance#) and an Australia Education Organization.
 
-- **Code**	  This contains the R development code. They are displayed in R markdown files as well as Jupyter Notebook files. 
+- **Code**	  This contains the R development code. They are displayed in R markdown files as well as Jupyter Notebook and html files. 
 
   - *dataStudentDropIndia*  This is the first part of the data science design pattern for student drop-out prediction, beginning with the task of preparing our data for building models using R.
   - *modelStudentDropIndia*  This is the second part of the data science design pattern for student drop-out prediction, introducing how to build multiple binary classification models using R.
@@ -41,5 +41,3 @@ The following is the directory structure for this design pattern:
 ## Build and Test
 The code is built by using R Markdown and then converted into Jupyter Notebook by using Notedown. 
 
-##Contribute
-Please open and issue if you find something that doesn't work as expected or have questions or suggestions. Note that this project is released with a [Guide to Contributing](CONTRIBUTING.md) and a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.