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

By: (Author) Fatwa Ramdani

Extended Catalogue

Ksh 11,700.00

Format: Hardback or Cased Book

ISBN-10: 9819646820

ISBN-13: 9789819646821

Publisher: Springer Nature Switzerland AG

Imprint: Springer Nature Switzerland AG

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Jun 19th, 2025

Print length: 417 Pages

Product Classification: Civil service & public sector
Databases

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  • Description

  • Reviews

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. Nowadays, most data includes location information, which can greatly enhance data science and decision-making.

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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