Textbook in PDF format 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. While many Data Science tools and programming languages are available, R stands out for its ease of learning, intuitive scripting, and the support of a vibrant, adaptive community. Its user-friendly interface, especially through RStudio, makes it an excellent choice for beginners and experienced users alike. Furthermore, although numerous books exist on Data Science, few highlight the unique power of spatial data—a critical and underexplored area in the field. This book seeks to fill that gap, offering in-depth explanations and step-by-step guidance on leveraging spatial data in research. 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. The first part of the Chapter 1 provides a strong foundation for understanding data science principles. Additionally, it walks through key steps in Data Science projects: defining objectives, planning, collecting and analyzing data, interpreting results, and effectively communicating complex findings to non-technical audiences. The second part covers the basics of R: what R is, why it’s useful, how to download and install it, an introduction to the R interface, and fundamental operations, including working with variables, data types, operators, and functions