acceleratoRs/GeneralTemplate

Data Science Accelerator - name of the accelerator.

Overview

A brief introduction of the acclerator.

The repository contains three parts

• Data This contains the provided sample data.
• Code This contains the R development code. They are displayed in R markdown files which can yield files of various formats.
• Docs This contains the documents, like blog, installation instructions, etc.

Business domain

Business domain of the data science problem. For example, predictive maintenance, customer churn, etc. It is better to use keywords instead of verbose description.

Data science problem

How the problem is formalized. For instance, a data science problem in a preditive maintenance application scenario may be to predict whether a machine is going to fail in operation after a certain number of running cycles.

Data understanding

A brief introduction of data used in the problem. Dont' have to be verbose as more detailed introduction will be put in the directory of /Data.

Modeling

How statistical or machine learning techniques are applied to resolve the data science problem.

Solution architecture

Overall solution architecture of the accelator. For instance, how a development pipeline is architectured for data pre-processing, model creating, and model deploying, for the data science problem.