Data Analytics: A Small Data Approach Contributor(s): Huang, Shuai (Author), Deng, Houtao (Author) |
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ISBN: 0367609509 ISBN-13: 9780367609504 Publisher: CRC Press OUR PRICE: $95.00 Product Type: Hardcover - Other Formats Published: April 2021 |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis - Science | Research & Methodology - Computers | Programming Languages - Python |
Dewey: 001.42 |
LCCN: 2021001775 |
Physical Information: 0.8" H x 8.3" W x 11.1" (2.60 lbs) 257 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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. |