Data Science: Foundations and Hands-on Experience
Handling Economic, Spatial, and Multidimensional Data with R
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Beschreibung
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.
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.
Equips the reader with a theoretical understanding of fundamentals of data science Provides hands-on practice with R using real-world data Features the handling of spatial data
Autor*in
Fatwa Ramdani
Themen in »Data Science: Foundations and Hands-on Experience«
R Programming Data Visualization Machine Learning Spatial Data Analysis Natural Language Processing