Linear Algebra and Its Applications with R

This book is designed to teach linear algebra to students in data science and bioinformatics programs, who may not realize the importance of the subject. It presents the topics in a traditional course, with lectures and lab exercises on simulated and empirical data sets. Each section starts with working examples to demonstrate how tools from linear algebra can help solve problems in applied sciences. The book is written from first principles and includes tutorials on how to install R, which is used primarily for computational examples. Code supplements are available for students who prefer Python.

Format: Hardback
Length: 406 pages
Publication date: 28 June 2021
Publisher: Taylor & Francis Ltd

This comprehensive textbook was developed with the specific purpose of teaching linear algebra to students enrolled in data science and bioinformatics programs. It is crucial to note that these students often overlook the significance of linear algebra in applied sciences, as traditional linear algebra courses primarily focus on mathematical concepts without adequately addressing the computational aspects or applications to data science and bioinformatics.

To address this gap, the author has designed a textbook that presents the topics in a traditional course format, complemented by lectures and lab exercises utilizing simulated and empirical data sets. This approach ensures that students gain a solid theoretical foundation that can be applied to real-world R and Python problems, equipping them with the necessary tools for practical applications.

Each section of the textbook begins with practical examples that illustrate how linear algebra tools can be employed to solve problems in applied sciences. These examples progress from simple computations, such as computing determinants of matrices, to more complex applications on simulated and empirical data sets using R. By incorporating computational examples throughout each section, students not only learn how to use R but also how to apply their knowledge to solve practical problems in the field.

Furthermore, this textbook is designed from first principles to emphasize the importance of linear algebra through practical computational examples with R and Python. In addition to comprehensive tutorials on installing R in the Appendix, code supplements are available for students who prefer a more comfortable learning environment with Python. R is primarily used for computational examples to develop students' practical computational skills.

The author of this textbook is a well-respected expert in the field of linear algebra, with extensive experience in teaching and research. Their expertise and passion for the subject shine through in the clear and concise writing style, making the textbook accessible to students of all backgrounds.

In conclusion, this textbook is an invaluable resource for students seeking to excel in data science and bioinformatics programs. By providing a comprehensive and practical approach to linear algebra, it equips students with the tools they need to solve complex problems and make meaningful contributions to their field. Whether a student is new to linear algebra or has some prior experience, this textbook offers a solid foundation that can help them achieve their academic and professional goals.

Weight: 788g
Dimension: 160 x 243 x 30 (mm)
ISBN-13: 9780367486846