データサイエンス：Ｒを用いて経済、空間、多次元データを扱う基礎と実地体験 Data Science: Foundations and Hands-on Experience : Handling Economic, Spatial, and Multidimensional Data with R

データサイエンス：Ｒを用いて経済、空間、多次元データを扱う基礎と実地体験<br>Data Science: Foundations and Hands-on Experience : Handling Economic, Spatial, and Multidimensional Data with R

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データサイエンス：Ｒを用いて経済、空間、多次元データを扱う基礎と実地体験
Data Science: Foundations and Hands-on Experience : Handling Economic, Spatial, and Multidimensional Data with R

Ramdani, Fatwa

ウェブストア価格 ¥17,710（本体¥16,100）
Springer Nature Switzerland AG（2025/06発売）
外貨定価 US$ 84.99
ポイント 161pt

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製本 Hardcover:ハードカバー版／ページ数 350 p.
言語 ENG
商品コード 9789819646821

Full Description

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.

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.