Intelligent Data Analysis: From Data Gathering to Data Comprehension

Collecting data is only the beginning; the harder task is making sense of it. Intelligent Data Analysis: From Data Gathering to Data Comprehension brings together chapters on the methods, tools, and ideas that help bridge that gap, from foundational statistics to modern approaches in data mining and machine learning.

Edited by Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna, and Kalpna Sagar, this multi-author volume connects analytical concepts with practical questions: what kinds of data are available, how can they be explored, and how can a model’s findings be understood?

From data types to analytical tools

The opening material introduces structured, semi-structured, and unstructured data, alongside big-data sources and common analytics phases. It surveys tools and platforms including Excel, Apache Spark, OpenRefine, R, Tableau, and Hadoop, and addresses database systems and challenges such as data storage, synchronization, and security.

Models that can be examined—and models that need explaining

A chapter on black-box and white-box modeling considers model interpretability and ways to explain predictions. Topics include linear regression and decision trees, as well as methods for examining less transparent models, such as partial dependence plots, individual conditional expectation, accumulated local effects, surrogate models, and feature importance.

Statistics, data mining, and visualization

Further chapters broaden the discussion to probability, descriptive and inferential statistics, data-mining methods, knowledge discovery, deep learning, and visualization. Together, these subjects show how analysis involves more than choosing an algorithm: it also requires understanding data, selecting suitable methods, and interpreting results.

For readers working with data

This reference-style collection will interest students and practitioners exploring data analytics, computational intelligence, big data, or machine learning. Its range of chapters offers several entry points, whether the reader wants a grounding in statistical ideas, a tour of analytical tools, or examples of how data analysis is applied to real-world problems.