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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

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IBM Watson Analytics: Automating Visualization, Descriptive, and Predictive Statistics.

Robert Eugene Hoyt¹, Dallas Snider², Carla Thompson³

¹College of HealthDepartment of Health Sciences and AdministrationUniversity of West FloridaPensacola, FLUnited States.

JMIR Public Health and Surveillance

|October 13, 2016

Summary

This summary is machine-generated.

IBM Watson Analytics (IBMWA) offers automated data analysis, including predictive and visual analytics, comparable to existing tools. While user-friendly, it requires data preprocessing and statistical knowledge for effective use.

Keywords:

data analysis data mining machine learning natural language processing statistical data analysis

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Area of Science:

Data Science and Analytics
Business Intelligence
Statistical Software

Background:

The era of big data necessitates advanced, efficient data analytics procedures.
IBM Watson Analytics (IBMWA) was developed to address these needs, offering advanced statistical capabilities.
IBMWA provides enhanced functions like automatic data analysis, quality examination, and optimal statistical approach determination.

Purpose of the Study:

To detail the features of IBM Watson Analytics (IBMWA).
To compare IBMWA with other data mining programs objectively and subjectively.

Main Methods:

Evaluation of IBM Watson Analytics (IBMWA) features.
Comparison with other analytical platforms using validated health datasets.

Main Results:

IBM Watson Analytics (IBMWA) produced predictions comparable to commercial and open-source software.
Visual analytics from IBMWA were similar to Microsoft Excel and Tableau.
IBMWA includes inherent data preprocessing and exploration assistance.

Conclusions:

IBM Watson Analytics (IBMWA) is a novel data analytics software automating descriptive, predictive, and visual analyses.
The software is user-friendly but necessitates data preprocessing, statistical understanding, and domain expertise.