Data Analytics A Small Data Approach-Test Bank

If you are looking to delve into the world of data analytics using a small data approach, the book Data Analytics: A Small Data Approach by Susan W. Hardwick could be a valuable resource for you. This book is designed for an introductory information analytics course, aiming to help students understand fundamental statistical learning models.

The book includes various small datasets that guide students in making decisions about the models and comparing their outcomes with results obtained from established R packages. It emphasizes the importance of exploratory data analysis, residual analysis, and flowcharts in developing and validating models and data pipelines as part of the data science process.

The key models covered in this book include linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal factor analysis, kernel methods such as the support vector machine and kernel regression, and deep learning. Each chapter introduces two or three methods, explaining the intuition and rationale behind them before diving into the mathematics and implementation using R on simulated and real-world datasets. Python code examples are also available.

ISBN Information:

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

Features of the Book:

• Introduction to fundamental statistical learning models
• Focus on small data approach
• Diverse range of models covered with practical implementation using R
• Inclusion of Python code examples
• Emphasis on exploratory data analysis and model validation

Who Should Read This Book?

Students and professionals interested in data analytics, particularly those looking to understand statistical learning models and their practical implementation using small datasets, would benefit from reading Data Analytics: A Small Data Approach.

FAQs (Frequently Asked Questions)

1. Is this book suitable for beginners in data analytics?

Yes, this book is suitable for beginners as it provides an introductory understanding of statistical learning models and their practical implementation using small datasets.

2. Are there practical examples and exercises in the book?

Yes, the book includes practical examples using R on both simulated and real-world datasets. It also offers Python code examples for implementation.

3. What sets this book apart from others on data analytics?

This book stands out for its focus on a small data approach, emphasizing the importance of exploratory data analysis and model validation in the data science process.

Conclusion

Data Analytics: A Small Data Approach by Susan W. Hardwick is a comprehensive guide for individuals looking to dive into the world of data analytics using statistical learning models. With a focus on practical implementation using small datasets, this book equips readers with the necessary knowledge and tools to understand and apply data analytics concepts effectively.