Introduction to Data Science provides a gentle introduction to data science for people of any discipline with little or no background in data analysis or computer programming. Based on notions of representation and modeling, the book examine some key data types and data structures, and allow you to learn to clean, transform, summarize and visualize data to communicate our results.
R for Data Science R for Data Science is a practical guide that serves as an excellent starting point for beginners. Authored by Hadley Wickham and Garrett Grolemund, this book introduces R programming for data analysis and visualization, with a strong emphasis on the tidyverse approach. Through hands-on examples and clear explanations, readers learn how to manipulate, explore, and visualize data effectively.
Modern R with the tidyverse takes a contemporary approach to R programming, focusing on modern packages, particularly those from the tidyverse ecosystem. Written by authors Thomas Mock and Rick Scavetta, this book goes beyond traditional R concepts, introducing readers to efficient data manipulation techniques and best practices for writing clean, readable code.
Practical Spatial Data is an invaluable resource for those interested in working with spatial data, particularly in coastal and marine environments. Authored by Masumbuko Semba, this book provides a comprehensive introduction to R programming with a specific focus on handling spatial data. From importing geographic information to performing spatial analysis, readers gain practical insights and hands-on experience in utilizing R for geospatial applications.
Geospatial Technology and Spatial Analysis in R delves deeper into the realm of geospatial data analysis using R. Written by Masumbuko Semba, this book explores the latest tools and packages available for modern spatial data handling and manipulation. Through step-by-step tutorials and real-world examples, readers learn how to harness the power of R for tasks such as geographic visualization, spatial statistics, and remote sensing analysis.
Data Science for Psychologists book provides an introduction to data science that is tailored to the needs of students in psychology, but is also suitable for students of the humanities and other biological or social sciences. This audience typically has a basic familiarity with statistics, but rarely an idea how data is prepared for statistical testing. By working with a variety of data types and many examples, this text teaches strategies and tools for reshaping, summarizing, and visualizing data.