Agile Data Science - Introduction

Agile data science is a methodology of utilizing data science with agile methodology for web application development. It focusses on the output of the data science process suitable for affecting change for an association. Data science incorporates building applications that describe research process with analysis, intelligent visualization and now applied machine learning as well.

The major objective of agile data science is to −

document and guide illustrative data analysis to discover and follow the critical path to a compelling product.

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

Continuous Iteration

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

Intermediate Output

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

Prototype Experiments

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

Integration of data

The software development life cycle incorporates various stages with data essential for −

  • clients

  • engineers, and

  • the business

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

Pyramid data value


The above pyramid value depicted the layers required for “Agile data science” development. It begins with a collection of records dependent on the prerequisites and plumbing individual records. The charts are created after cleaning and aggregation of data. The aggregated data can be used for data visualization. Reports are generated with appropriate 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 successfully. The best representation of this implementation is “Artificial Intelligence”.

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