Data Analytics

A Small Data Approach

By: Houtao Deng, Shuai Huang

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Hardcover | 20 April 2021 | Edition Number 1

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Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and then compare with results obtained from established R packages. Also, as data science practice is a process that should be told as a story, in this book there are many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines.

The main models covered in this book include linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal component analysis, kernel methods including the support vector machine and kernel regression, and deep learning. Each chapter introduces two or three techniques. For each technique, the book highlights the intuition and rationale first, then shows how mathematics is used to articulate the intuition and formulate the learning problem. R is used to implement the techniques on both simulated and real-world dataset. Python code is also available at the book's website: http: //dataanalyticsbook.info.

Industry Reviews

"Another strength of the book is that the authors cover the regression methods comprehensively, starting from the relationship between variables, to the connections between methods. As a result, this book may be an introductory guide for health care professionals, students, and lecturers, both by showing the exercises with manual solutions and giving the R coding of the methods."
-Selen Yilmaz Isikhan in ?ISCB?, September 2022

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