This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.
This Website Uses Cookies
By closing this message or continuing to use our site, you agree to our cookie policy. Learn More
This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.
Mission Critical logo
search
cart
facebook   twitter   linkedin   youtube  
  • Sign In
  • Create Account
  • Sign Out
  • My Account
Mission Critical logo
  • NEWS
    • Industry News
    • Women in Tech
    • Events Calendar
  • PRODUCTS
    • New Products
    • Editor’s Pick
  • TECHNOLOGIES
    • Cloud Computing
    • Cybersecurity
    • Edge Computing
    • Infrastructure
    • Power
    • 5G
  • OPERATIONS
    • Building Commissioning
    • Facility Design
    • Life Safety
    • Mission Sustainability
      • Code Green
    • Site Selection
    • Thermal Management
  • COLUMNS
    • Writing on the Edge
    • Unconventional Wisdom
    • On Target Series
    • Guest Column
      • Hot Aisle Insight
      • MC Skills Exchange
      • Real (Estate) Talk
  • MULTIMEDIA
    • Podcasts
    • Videos
    • Webinars
    • eNewsletter
  • CONTESTS
    • Top Tier Product Awards
    • Women in Technology
  • RESOURCES
    • Buyer's Guide
    • White Papers
    • Quizzes
    • Case Studies
    • Technical Advisory Board
    • Continuing Education
    • Store
    • Student Section
      • Training and Education
      • Glossary
    • Classifieds
    • DCEP
    • International Data Center Day
  • EMAGAZINE
    • eMagazine
    • Archived Issues
    • Contact Us
    • Advertise
    • Subscribe
Home » Store » Books » Data Analytics: A Small Data Approach
9780367609504.jpg

Data Analytics: A Small Data Approach

$89.95
Books

Product Details

ISBN 9780367609504
Published April 16, 2021
273 Pages
 

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.

Table of Contents

1. INTRODUCTION

Who will benefit from this book

Overview of a Data Analytics Pipeline

Topics in a Nutshell

2. ABSTRACTION

Regression & tree models

Overview

Regression Models

Tree Models

Remarks

Exercises

3. RECOGNITION

Logistic regression & ranking

Overview

Logistic Regression Model

A Ranking Problem by Pairwise Comparison

Statistical Process Control using Decision Tree

Remarks

Exercise

4. RESONANCE

Bootstrap & random forests

Overview

How Bootstrap Works

Random Forests

Remarks

Exercises

5. LEARNING (I)

Cross validation & OOB

Overview

Cross-Validation

Out-of-bag error in Random Forest

Remarks

Exercises

6. DIAGNOSIS

Residuals & heterogeneity

Overview

Diagnosis in Regression

Diagnosis in Random Forests

Clustering

Remarks

Exercises

7. LEARNING (II)

SVM & ensemble Learning

Overview

Support Vector Machine

Ensemble Learning

Remarks

Exercises

data analytics

8. SCALABILITY

LASSO & PCA

Overview

LASSO

Principal Component Analysis

Remarks

Exercises

9. PRAGMATISM

Experience & experimental

Overview

Kernel Regression Model

Conditional Variance Regression Model

Remarks

Exercises

10. SYNTHESIS

Architecture & pipeline

Overview

Deep Learning

inTrees

Remarks

Exercises

CONCLUSION

APPENDIX: A BRIEF REVIEW OF BACKGROUND KNOWLEDGE

The normal distribution

Matrix operations

Optimization

Get our new eMagazine delivered to your inbox every month.

Stay in the know on the latest data center news and information.

SUBSCRIBE TODAY!
  • Resources
    • Product Info (Free)
    • Security Group
    • Editorial Guidelines
    • List Rental
    • Survey And Sample
    • Custom Content & Marketing
    • Market Research
    • Partners
  • Want More
    • Connect
  • Privacy
    • PRIVACY POLICY
    • TERMS & CONDITIONS
    • DO NOT SELL MY PERSONAL INFORMATION
    • PRIVACY REQUEST
    • UPDATE MY PREFERENCES
    • ACCESSIBILITY

Copyright ©2023. All Rights Reserved BNP Media.

Design, CMS, Hosting & Web Development :: ePublishing