Practitioner’s Guide to Data Science: Streamlining Data Science Solutions Using Python, Scikit-Learn, and Azure ML Service Platform by Nasir Ali Mirza

Author:Nasir Ali Mirza [Mirza, Nasir Ali]
Language: eng
Format: epub
Publisher: BPB Publications
Published: 2022-08-15T00:00:00+00:00

Points to remember

Data exploration and visualization is an essential Data Science skill that helps in gaining the critical insights into the data for performing data preparation and feature engineering.

CRISP-DM calls this phase of the project as data understanding and breaks its activities into collecting initial data, describing, exploring, and verifying data quality.

TDSP names this stage of the project as data acquisition and understanding and divides its activities into goals, tasks, and deliverables.

Data acquisition needs a careful planning and design with appropriate selection of tools for data movement for the given source and analytics platform.

Sampling of source data is required if the data size is large for performing the data exploration and visualization, and this needs to be done without losing essential information in the data.

Data exploration comprises of performing data distribution analysis as well as exploring simple and complex relations in the data using appropriate visualizations for univariate, bivariate, and multivariate analysis.

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