Buy Data Analytics: A Small Data Approach
1st Edition PDF ebook by author Shuai Huang; Houtao Deng – published by Chapman & Hall in 2021 and save up to 80% compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.
You can search our site for other versions of the Data Analytics: A Small Data Approach
1st Edition PDF ebook. You can also search for others PDF ebooks from publisher Chapman & Hall, as well as from your favorite authors. We have thousands of online textbooks and course materials (mostly in PDF) that you can download immediately after purchase.
Note: e-textBooks do not come with access codes, CDs/DVDs, workbooks, and other supplemental items.
eBook Details:
Full title: Data Analytics: A Small Data Approach
1st Edition
Edition: 1st
Copyright year: 2021
Publisher: Chapman & Hall
Author: Shuai Huang; Houtao Deng
ISBN: 9780367609511, 9781000372489
Format: PDF
Description of Data Analytics: A Small Data Approach
1st Edition:
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.
