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Data science : foundations and hands-on experience = handling economic, spatial, and multidimensional data with R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science : foundations and hands-on experience/ by Fatwa Ramdani.
Reminder of title:	handling economic, spatial, and multidimensional data with R /
Author:	Ramdani, Fatwa.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xiv, 417 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Electronic data processing. -
Online resource:	https://doi.org/10.1007/978-981-96-4683-8
ISBN:	9789819646838

Data science : foundations and hands-on experience = handling economic, spatial, and multidimensional data with R /
Ramdani, Fatwa.

Data science : foundations and hands-on experiencehandling economic, spatial, and multidimensional data with R /[electronic resource] :by Fatwa Ramdani. - Singapore :Springer Nature Singapore :2025. - xiv, 417 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9789819646838

Standard No.: 10.1007/978-981-96-4683-8doiSubjects--Topical Terms:

520749
Electronic data processing.

LC Class. No.: QA76

Dewey Class. No.: 004

Data science : foundations and hands-on experience = handling economic, spatial, and multidimensional data with R /
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505 0 $a 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.
520 $a 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.
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