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| 标题（Title） | 37 个字符（一般不超过80个字符） |
Data Analytics: A Small Data Approach
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| 描述（Description） | 673 个字符（一般不超过200个字符） |
This book is suitable for an introductory course of data analytics to help students understand some main statistical learning models, such as 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, etc. Data science practice is a process that should be told as a story, rather than a one-time implementation of one single model. This process is a main focus of this book, with many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines.
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