Telling Stories with Data: With Applications in R

The book provides students with the skills needed to do data science, including gathering, cleaning, preparing, and sharing data, using statistical models to analyze data, writing about the results, drawing conclusions, and using the cloud to put a model into production. It is based on a workflow conducted in an ethical and reproducible way, with extensive case studies and formative assessment.

Format: Hardback
Length: 598 pages
Publication date: 28 June 2023
Publisher: Taylor & Francis Ltd

The book is a comprehensive resource for students seeking to acquire the end-to-end skills required for data science. It encompasses a range of activities, including data gathering, cleaning, preparation, analysis, and sharing, utilizing statistical models to interpret data and draw meaningful conclusions. Moreover, it emphasizes the importance of reproducibility in data science, ensuring that the processes and methodologies employed are transparent, ethical, and can be replicated by others.

One of the key strengths of the book is its extensive coverage of code examples. These examples provide practical insights into the various techniques and algorithms used in data science, allowing students to apply their knowledge in real-world scenarios. Additionally, the book integrates ethics throughout, emphasizing the responsible and ethical use of data and the importance of considering societal implications and ethical considerations in data analysis.

Reproducibility is another critical aspect of data science, and the book emphasizes its importance throughout. It provides guidance on how to set up and maintain reproducible environments, ensuring that the results of data analysis can be replicated and verified by others. The book also includes extensive formative assessment throughout, allowing students to test their understanding and reinforce their learning.

In terms of its content, the book focuses on teaching students how to gather, prepare, and analyze data, utilizing statistical models to interpret and understand the data. It covers a range of topics, including data visualization, machine learning, and natural language processing, providing students with a comprehensive understanding of the field. Additionally, the book covers the writing of data science reports and manuscripts, emphasizing the importance of clear and concise communication of findings.

The use of GitHub and the open-source statistical language R is built into the book throughout. GitHub is a popular platform for sharing and collaborating on code, and R is a powerful statistical language widely used in data science. By integrating these tools into the book, students are provided with the opportunity to practice their skills and collaborate with others in the data science community.

Overall, the book is a valuable resource for students seeking to pursue a career in data science. Its comprehensive coverage of skills, emphasis on reproducibility, and integration of ethics and GitHub make it an essential tool for anyone interested in advancing their knowledge and skills in this field.

Weight: 453g
Dimension: 254 x 178 (mm)
ISBN-13: 9781032134772