Data Analytics A Small Data Approach

Data Analytics: A Small Data Approach is a comprehensive textbook suitable for introductory information analytics courses. It provides a detailed understanding of various statistical learning models using small datasets. The book equips students with the knowledge to analyze data, make informed decisions, and validate models using R programming.

The key topics covered in the book include linear regression, logistic regression, tree models, random forests, ensemble learning, sparse learning, principal factor analysis, kernel methods (such as support vector machines and kernel regression), and deep learning. Each chapter introduces two or three methods, focusing on providing intuition, rationale, mathematical articulation, and practical implementation using R programming.

Readers will benefit from the extensive course materials available in the book, covering exploratory data analysis, residual analysis, and flowcharts for model development and validation. Additionally, Python code examples are provided for further learning and application.

ISBN Information

• ISBN-10: 0367609509
• ISBN-13: 978-0367609504

FAQs

What is the target audience for this book?

The book is suitable for students taking introductory information analytics courses and individuals looking to gain a practical understanding of data analytics using small datasets.

Does the book include practical examples?

Yes, the book includes practical examples using both simulated and real-world datasets. Readers can implement the discussed methods using R programming.

Are Python code examples available in the book?

Yes, Python code examples are provided in the book to enhance learning and application of the discussed statistical learning models.

Conclusion

Data Analytics: A Small Data Approach offers a thorough introduction to various statistical learning models and their practical implementation using R programming. With a focus on small datasets, the book equips readers with the skills to analyze data, develop models, and validate results effectively. Whether you are a student or a professional seeking to enhance your data analytics skills, this book provides valuable insights and practical knowledge in the field of data analytics.