Data Science: Foundations and Hands-on Experience: Handling Economic, Spatial, and Multidimensional Data with R

by Fatwa Ramdani

Overview

This book will take readers from foundational concepts to practical applications, enabling them to transform raw data into meaningful insights. It covers key skills such as data collection, cleaning, organization, exploration, analysis, and impactful presentation—core competencies for navigating today’s data-rich landscape.

 Each chapter is designed to build both theoretical understanding and hands-on expertise. The book’s unique dual-approach structure introduces foundational data science concepts, followed by exercises in RStudio using real-world datasets from social fields. This blend of theory and practice ensures readers grasp the ‘how’ and the ‘why’ behind data-driven research, making it ideal for students, researchers, and professionals seeking to enhance their analytical capabilities. Spatial data analysis stands out as one of the most unique in this book because it focuses on spatial data, a topic rarely covered in data science references. While there are many resources on data science, few explore the unique aspects of spatial data. Nowadays, most data includes location information, which can greatly enhance data science and decision-making.

The final chapter will discuss critical topics in data ethics and reproducibility, encouraging readers to think responsibly about data use. By the end, readers will gain not only technical skills but also ethical awareness, empowering them to conduct rigorous, reliable, and socially conscious research. No prior experience with data science is required—just an eagerness to explore the power of data in understanding and shaping society. This textbook is suitable for adoption in both undergraduate and graduate classes. The book will help students build a solid theoretical foundation in data science while gaining hands-on experience with RStudio.

Product Details

ISBN-13:	9789819646838
Publisher:	Springer-Verlag New York, LLC
Publication date:	06/18/2025
Sold by:	Barnes & Noble
Format:	eBook
File size:	49 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Fatwa Ramdani is currently a Professor at the University of Tsukuba, Japan, within the Graduate School of Humanities and Social Sciences. A graduate of Tohoku University, Japan, his research expertise lies in Geoinformatics, combining geography with information science and technology to analyze spatial phenomena and geospatial datasets. His work provides unique insights into changes in the human-physical environment, addressing fundamental challenges in physical, environmental, and social sciences.  Before joining the University of Tsukuba, Dr. Ramdani was a lecturer in the Information Systems Department at Brawijaya University, Indonesia, where he also served as the Director of the Geoinformatics Research Group. Dr. Ramdani has authored several books, including "Exploring the Earth with QGIS: A Guide to Using Satellite Imagery at Its Full Potential," published by SpringerNature. He has also published numerous peer-reviewed articles in conferences and journals, contributing significantly to the field of environmental impact assessment and geospatial data analysis.  He is affiliated with esteemed organizations such as the IEEE Geoscience and Remote Sensing Society (IGARSS), the Japan Society of Photogrammetry and Remote Sensing (JSPRS), the Remote Sensing Society of Japan (RSSJ), Japan Geographic Information System Association (GISA) and the European Geosciences Union (EGU). Dr. Ramdani actively serves as an editor and reviewer for various journals, including "Nature", "Climatic Change", "Remote Sensing," "Applied Sciences," "AgriEngineering", and "Network: Computation in Neural Systems."  In addition to his research, Dr. Ramdani teaches courses such as "Data Science for Social Sciences" and "Introduction to Geographic Information Systems" at the University of Tsukuba. His dedication to advancing geospatial science and technology continues to contribute to understanding and addressing environmental and societal challenges.

Table of Contents

Introduction to Data Science & Process of Data Science.- Data Types & Measurement Scale.- Data Exploration, Preprocessing, & Modeling.- Statistics - Descriptive & Inferential.- Data Visualization & Uncertainty.- Machine Learning, Measuring Uncertainty, and Forecasting.- Working with Spatial Data.- Web Scraping & Data Mining.- Natural Language Processing & Sentiment Analysis.- Ethics & Reproducibility.