About the Book: 

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.

Contents:

1.    Introduction

2.    Abstraction

3.    Recognition

4.    Resonance

5.    Learning (i)

6.    Diagnosis

7.    Learning (ii)

8.    Scalability

9.    Pragmatism

10.    Synthesis

About the Authors:

Shuai Huang is an associate professor at the department of industrial & systems engineering at the university of Washington. He conducts interdisciplinary research in machine learning, data analytics, and applied operations research with applications on healthcare, manufacturing, and transportation areas.