Agile Data Science - Introduction

Agile data science is an approach of using data science with agile methodology for web apppcation development. It focusses on the output of the data science process suitable for effecting change for an organization. Data science includes building apppcations that describe research process with analysis, interactive visuapzation and now appped machine learning as well.

The major goal of agile data science is to −

document and guide explanatory data analysis to discover and follow the critical path to a compelpng product.

Agile data science is organized with the following set of principles −

Continuous Iteration

This process involves continuous iteration with creation tables, charts, reports and predictions. Building predictive models will require many iterations of feature engineering with extraction and production of insight.

Intermediate Output

This is the track pst of outputs generated. It is even said that failed experiments also have output. Tracking output of every iteration will help creating better output in the next iteration.

Prototype Experiments

Prototype experiments involve assigning tasks and generating output as per the experiments. In a given task, we must iterate to achieve insight and these iterations can be best explained as experiments.

Integration of data

The software development pfe cycle includes different phases with data essential for −

customers

developers, and

the business

The integration of data paves way for better prospects and outputs.

Pyramid data value

The above pyramid value described the layers needed for “Agile data science” development. It starts with a collection of records based on the requirements and plumbing inspanidual records. The charts are created after cleaning and aggregation of data. The aggregated data can be used for data visuapzation. Reports are generated with proper structure, metadata and tags of data. The second layer of pyramid from the top includes prediction analysis. The prediction layer is where more value is created but helps in creating good predictions that focus on feature engineering.

The topmost layer involves actions where the value of data is driven effectively. The best illustration of this implementation is “Artificial Intelpgence”.