Data Science: Theory, Analysis, and Applications

Data science is not one method applied to one kind of problem. This edited collection shows the field at work across databases, healthcare, bioinformatics, image analysis, text, and video. Its fourteen chapters offer a research-led look at the algorithms, analytical approaches, and applications that connect large or complex datasets with practical questions.

Qurban A. Memon and Shakeel Ahmed Khoja organize the volume in three parts, moving from foundational concepts and algorithms through data design and analysis to applications and newer directions. The result is a wide-ranging reference for readers interested in how data-science techniques are adapted across disciplines.

From concepts to real-world data problems

The opening section covers topics including geospatial query processing with MongoDB and Spark, metaheuristic neural networks for image segmentation, feature selection, NoSQL database design, and distributed stream-data collection. Together, these chapters explore both the systems that handle data and the computational methods used to analyze it.

Analysis across health and biology

The middle section turns to data design and analysis, with chapters on big-data management in healthcare, analytics using the Framingham Heart Study, mutated cardiac ion-channel proteins associated with Brugada syndrome, image-based burn classification, and emotion classification in brain-computer interfacing. These varied studies illustrate how analytical questions and data types differ from one application area to another.

New applications in text, images, and video

The final section considers writer retrieval from handwritten documents, guest-satisfaction classification using reviews and ratings, machine-learning methods for sentiment analysis, and emotion recognition from continuous-action video. The collection’s breadth makes it useful for comparing research problems and approaches across several data-rich fields.

Who may find this collection useful

This research-oriented volume is intended for researchers, professors, research students, and practitioners in data science and related areas. Some chapters take a tutorial-style approach, while others present focused studies; readers can consult the sections most relevant to their interests.

For readers looking beyond a single tool or application, Data Science: Theory, Analysis, and Applications offers a substantial cross-section of the questions researchers are addressing with data-driven methods.