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Exploratory Data Analysis with R

Editorial Reviews

This book covers some of the basics of visualizing data in R and summarizing high dimensional data with statistical multivariate analysis techniques. There is less of an emphasis on formal statistical inference methods, as inference is typically not the focus of EDA. Rather, the goal is to show the data, summarize the evidence and identify interesting patterns while eliminating ideas that likely won’t pan out.

Throughout the book, we will focus on the R statistical programming language. We will cover the various plotting systems in R and how to use them effectively. We will also discuss how to implement dimension reduction techniques like clustering and the singular value decomposition. All of these techniques will help you to visualize your data and to help you make key decisions in any data analysis.

Table of Contents

  • Getting Started with R
  • Managing Data Frames with the dplyr package
  • Exploratory Data Analysis Checklist
  • Principles of Analytic Graphics
  • Exploratory Graphs
  • Plotting Systems
  • Graphics Devices
  • The Base Plotting System
  • The ggplot2 Plotting System: Part 1
  • The ggplot2 Plotting System: Part 2
  • Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S.

Book Details

  • Author:
  • Pages: 125 pages
  • Publication Date: 2015-06-23
  • Publisher:
  • Language: English

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Data Mining for Business Intelligence, 2nd Edition

Editorial Reviews

Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data.
From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization.
The Second Edition now features:

  • Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles
  • A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice
  • Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods
  • Summaries at the start of each chapter that supply an outline of key topics

The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions.
Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Table of Contents

Part One: Preliminaries
Chapter 1: Introduction
Chapter 2: Overview of the Data Mining Process

Part Two: Data Exploration and Dimension Reduction
Chapter 3: Data Visualization
Chapter 4: Dimension Reduction

Part Three: Performance Evaluation
Chapter 5: Evaluating Classification and Predictive Performance

Part Four: Prediction and Classification Methods
Chapter 6: Multiple Linear Regression
Chapter 7: k-Nearest Neighbors (k-NN)
Chapter 8: Naive Bayes
Chapter 9: Classification and Regression Trees
Chapter 10: Logistic Regression
Chapter 11: Neural Nets
Chapter 12: Discriminant Analysis

Part Five: Mining Relationships Among Records
Chapter 13: Association Rules
Chapter 14: Cluster Analysis

Part Six: Forecasting Time Series
Chapter 15: Handling Time Series
Chapter 16: Regression-Based Forecasting
Chapter 17: Smoothing Methods

Part Seven: Cases
Chapter 18: Cases

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Intelligent Knowledge: A Study beyond Data Mining

Editorial Reviews

This book is mainly about an innovative and fundamental method called “intelligent knowledge” to bridge the gap between data mining and knowledge management, two important fields recognized by the information technology (IT) community and business analytics (BA) community respectively. The book includes definitions of the “first-order” analytic process, “second-order” analytic process and intelligent knowledge, which have not formally been addressed by either data mining or knowledge management. Based on these concepts, which are especially important in connection with the current Big Data movement, the book describes a framework of domain-driven intelligent knowledge discovery. To illustrate its technical advantages for large-scale data, the book employs established approaches, such as Multiple Criteria Programming, Support Vector Machine and Decision Tree to identify intelligent knowledge incorporated with human knowledge. The book further shows its applicability by means of real-life data analyses in the contexts of internet business and traditional Chinese medicines.

Table of Contents

Chapter 1 Data Mining and Knowledge Management
Chapter 2 Foundations of Intelligent Knowledge Management
Chapter 3 Intelligent Knowledge and Habitual Domain
Chapter 4 Domain Driven Intelligent Knowledge Discovery
Chapter 5 Knowledge-incorporated Multiple Criteria Linear Programming Classifiers
Chapter 6 Knowledge Extraction from Support Vector Machines
Chapter 7 Intelligent Knowledge Acquisition and Application in Customer Churn
Chapter 8 Intelligent Knowledge Management in Expert Mining in Traditional Chinese Medicines

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Convex Optimization Algorithms

Editorial Reviews

This book, developed through class instruction at MIT over the last 15 years, provides an accessible, concise, and intuitive presentation of algorithms for solving convex optimization problems. It relies on rigorous mathematical analysis, but also aims at an intuitive exposition that makes use of visualization where possible. This is facilitated by the extensive use of analytical and algorithmic concepts of duality, which by nature lend themselves to geometrical interpretation. The book places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning.

Among its features, the book:

  • Develops comprehensively the theory of descent and approximation methods, including gradient and subgradient projection methods, cutting plane and simplicial decomposition methods, and proximal methods
  • Describes and analyzes augmented Lagrangian methods, and alternating direction methods of multipliers
  • Develops the modern theory of coordinate descent methods, including distributed asynchronous convergence analysis
  • Comprehensively covers incremental gradient, subgradient, proximal, and constraint projection methods
  • Includes optimal algorithms based on extrapolation techniques, and associated rate of convergence analysis
  • Describes a broad variety of applications of large-scale optimization and machine learning
  • Contains many examples, illustrations, and exercises
  • Is structured to be used conveniently either as a standalone text for a class on convex analysis and optimization, or as a theoretical supplement to either an applications/convex optimization models class or a nonlinear programming class

Table of Contents

Chapter 1. Convex Optimization Models: An Overview
Chapter 2. Optimization Algorithms: An Overview
Chapter 3. Subgradient Methods
Chapter 4. Polyhedral Approximation Methods
Chapter 5. Proximal Algorithms
Chapter 6. Additional Algorithmic Topics

Chapter Appendix A: Mathematical Background
Chapter Appendix B: Convex Optimization Theory: A Summary

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Parallel Computing for Data Science: With Examples in R, C++ and CUDA

Editorial Reviews

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic “n observations, p variables” matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.

With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.

Table of Contents

Chapter 1: Introduction to Parallel Processing in R
Chapter 2: “Why Is My Program So Slow?”: Obstacles to Speed
Chapter 3: Principles of Parallel Loop Scheduling
Chapter 4: The Shared-Memory Paradigm: A Gentle Introduction via R
Chapter 5: The Shared-Memory Paradigm: C Level
Chapter 6: The Shared-Memory Paradigm: GPUs
Chapter 7: Thrust and Rth
Chapter 8: The Message Passing Paradigm
Chapter 9: MapReduce Computation
Chapter 10: Parallel Sorting and Merging
Chapter 11: Parallel Pre x Scan
Chapter 12: Parallel Matrix Operations
Chapter 13: Inherently Statistical Approaches: Subset Methods
Appendix A: Review of Matrix Algebra
Appendix B: R Quick Start
Appendix C: Introduction to C for R Programmers

Book Details

  • Author:
  • Pages: 328 pages
  • Edition: 1
  • Publication Date: 2015-06-23
  • Publisher:
  • Language: English
  • ISBN-10: 1466587016
  • ISBN-13: 9781466587014

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Professional Web Design: Techniques and Templates, 5th Edition

Editorial Reviews

If you are a web designer, or are on your way to becoming one, you know the importance of staying up-to-date with the latest tools, techniques, and trends of the business. Learning CSS technology and continually improving your design and developer skills are essential elements of your success. In this fifth edition of PROFESSIONAL WEB DESIGN: TECHNIQUES AND TEMPLATES, popular author and web developer Clint Eccher teaches beginning to intermediate web designers, through case studies and helpful tips and techniques, how to create attractive, fast, and efficient websites. In addition to the helpful instruction, readers will also have access to more than 200 completely customizable design templates. Eccher covers essential topics, from understanding how web development works today to working with clients, enhancing usability, understanding graphics, and much more. The included case studies cover a variety of requirements and design types. Get up to speed, and up to date, with modern web design–get PROFESSIONAL WEB DESIGN: TECHNIQUES AND TEMPLATES, FIFTH EDITION.

Table of Contents

Chapter 1 Overview of Web Development Today
Chapter 2 Designing for the Past, Present, and Future
Chapter 3 Things to Consider Before Beginning
Chapter 4 Enhancing Usability
Chapter 5 Gathering Requirements and Creating a Comp
Chapter 6 What Is Needed to Build Mortised Sites
Chapter 7 Understanding Graphics
Chapter 8 Creating CSS Designs
Chapter 9 Case Study: Low-Content CSS Design
Chapter 10 Case Study: Medium-Content CSS Design
Chapter 11 Case Study: High-Content CSS Design
Chapter 12 Case Study: Full-Height Three-Column Layout
Chapter 13 Case Study: Background-Based Design
Chapter 14 Case Study: A CSS Form
Chapter 15 Tips and Techniques
Chapter 16 Customizing the Designs Included in This Book
Chapter 17 Templates Included Online

Book Details

  • Author:
  • Pages: 688 pages
  • Edition: 5
  • Publication Date: 2014-07-09
  • Publisher:
  • Language: English
  • ISBN-10: 1305257529
  • ISBN-13: 9781305257528

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The Hacker Playbook 2: Practical Guide To Penetration Testing

Editorial Reviews

Just as a professional athlete doesn’t show up without a solid game plan, ethical hackers, IT professionals, and security researchers should not be unprepared, either. The Hacker Playbook provides them their own game plans. Written by a longtime security professional and CEO of Secure Planet, LLC, this step-by-step guide to the “game” of penetration hacking features hands-on examples and helpful advice from the top of the field.

Through a series of football-style “plays,” this straightforward guide gets to the root of many of the roadblocks people may face while penetration testing—including attacking different types of networks, pivoting through security controls, privilege escalation, and evading antivirus software.

From “Pregame” research to “The Drive” and “The Lateral Pass,” the practical plays listed can be read in order or referenced as needed. Either way, the valuable advice within will put you in the mindset of a penetration tester of a Fortune 500 company, regardless of your career or level of experience.

This second version of The Hacker Playbook takes all the best “plays” from the original book and incorporates the latest attacks, tools, and lessons learned. Double the content compared to its predecessor, this guide further outlines building a lab, walks through test cases for attacks, and provides more customized code.

Whether you’re downing energy drinks while desperately looking for an exploit, or preparing for an exciting new job in IT security, this guide is an essential part of any ethical hacker’s library—so there’s no reason not to get in the game.

Table of Contents

  • Introduction
  • Pregame – The Setup
  • The Drive – Exploiting Scanner Findings
  • The Lateral Pass – Moving Through The Network
  • The Screen – Social Engineering
  • The Onside Kick – Attacks That Require Physical Access
  • The Quarterback Sneak – Evading AV
  • Special Teams – Cracking, Exploits, And Tricks

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Geocomputation: A Practical Primer

Editorial Reviews

“Brunsdon and Singleton offer a unique contribution to the zeitgeist of geocomputation… The authors offer a wide array of applications brought by leading scholars in the field of Geographic Information Science, spatial analysis and spatial modelling. The role of new techniques that are revolutionizing the usage of geocomputation is well explored and the systematic approach the book adopts in envisioning available tools is appropriately constructed. This book is a great contribution for an advancing field, and a much welcomed achievement for the growth of a new kind of spatial science.”
– Eric Vaz Director of the Laboratory for Geocomputation, Ryerson University

Geocomputation is the use of software and computing power to solve complex spatial problems. It is gaining increasing importance in the era of the ‘big data’ revolution, of ‘smart cities’, of crowdsourced data, and of associated applications for viewing and managing data geographically – like Google Maps. This student focused book:

  • Provides a selection of practical examples of geocomputational techniques and ‘hot topics’ written by world leading practitioners.
  • Integrates supporting materials in each chapter, such as code and data, enabling readers to work through the examples themselves.

Chapters provide highly applied and practical discussions of:

  • Visualisation and exploratory spatial data analysis
  • Space time modelling
  • Spatial algorithms
  • Spatial regression and statistics
  • Enabling interactions through the use of neogeography

All chapters are uniform in design and each includes an introduction, case studies, conclusions – drawing together the generalities of the introduction and specific findings from the case study application – and guidance for further reading.

This accessible text has been specifically designed for those readers who are new to Geocomputation as an area of research, showing how complex real-world problems can be solved through the integration of technology, data, and geocomputational methods. This is the applied primer for Geocomputation in the social sciences.

Table of Contents

PART I DESCRIBING HOW THE WORLD LOOKS
Chapter 1 Spatial Data Visualisation with R
Chapter 2 Geographical Agents in Three Dimensions
Chapter 3 Scale, Power Laws, and Rank Size in Spatial Analysis

PART II EXPLORING MOVEMENTS IN SPACE
Chapter 4 Agent-Based Modeling and Geographical Information Systems
Chapter 5 Microsimulation Modelling for Social Scientists
Chapter 6 Spatio-Temporal Knowledge Discovery
Chapter 7 Circular Statistics

PART III MAKING GEOGRAPHICAL DECISIONS
Chapter 8 Geodemographic Analysis
Chapter 9 Social Area Analysis and Self-Organizing Maps
Chapter 10 Kernel Density Estimation and Percent Volume Contours
Chapter 11 Location-Allocation Models

PART IV EXPLAINING HOW THE WORLD WORKS
Chapter 12 Geographically Weighted Generalised Linear Modelling
Chapter 13 Spatial Interaction Models
Chapter 14 Python Spatial Analysis Library (PySAL): An Update and Illustration
Chapter 15 Reproducible Research: Concepts, Techniques and Issues

PART V ENABLING INTERACTIONS
Chapter 16 Using Crowd-Sourced Information to Analyse Changes in the Onset of the North American Spring
Chapter 17 Open Source GIS Software
Chapter 18 Public Participation in Geocomputation to Support Spatial Decision-Making

Book Details

  • Pages: 392 pages
  • Edition: 1
  • Publication Date: 2015-02-05
  • Publisher:
  • Language: English
  • ISBN-10: 1446272931
  • ISBN-13: 9781446272930

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Mastering Pandas

Editorial Reviews

Master the features and capabilities of pandas, a data analysis toolkit for Python

About This Book

  • Master and optimally utilize the capabilities of Pandas for data analysis using IPython a rich interactive environment for Python
  • Understand data visualization by plotting data with matplotlib
  • Learn predictive analytics and machine learning using pandas and scikit-learn in a pragmatic manner

Who This Book Is For

This book is intended for Python programmers, mathematicians, and analysts who already have a basic understanding of Python and wish to learn about its data analysis capabilities in depth.

In Detail

Python is a ground breaking language for its simplicity and succinctness, allowing the user to achieve a great deal with a few lines of code, especially compared to other programming languages. The pandas brings these features of Python into the data analysis realm, by providing expressiveness, simplicity, and powerful capabilities for the task of data analysis. By mastering pandas, users will be able to do complex data analysis in a short period of time, as well as illustrate their findings using the rich visualization capabilities of related tools such as IPython and matplotlib.

This book is an in-depth guide to the use of pandas for data analysis, for either the seasoned data analysis practitioner or the novice user. It provides a basic introduction to the pandas framework, and takes users through the installation of the library and the IPython interactive environment. Thereafter, you will learn basic as well as advanced features, such as MultiIndexing, modifying data structures, and sampling data, which provide powerful capabilities for data analysis.

Table of Contents

Chapter 1. Introduction to pandas and Data Analysis
Chapter 2. Installation of pandas and the Supporting Software
Chapter 3. The pandas Data Structures
Chapter 4 V’s of big data
Chapter 4. Operations in pandas, Part I – Indexing and Selecting
Chapter 5. Operations in pandas, Part II – Grouping, Merging, and Reshaping of Data
Chapter 6. Missing Data, Time Series, and Plotting Using Matplotlib
Chapter 7. A Tour of Statistics – The Classical Approach
Chapter 8. A Brief Tour of Bayesian Statistics
Chapter 9. The pandas Library Architecture
Chapter 10. R and pandas Compared
Chapter 11. Brief Tour of Machine Learning

Book Details

  • Author:
  • Pages: 352 pages
  • Edition: 1
  • Publication Date: 2015-06-22
  • Publisher:
  • Language: English
  • ISBN-10: 1783981962
  • ISBN-13: 9781783981960

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Raspberry Pi Networking Cookbook

Editorial Reviews

Computer expert or enthusiast, this cookbook will help you use your Raspberry Pi to enhance your existing network. From sharing media across devices to deploying your own web portal, you’ll be amazed at what can be achieved.

Overview

  • Learn how to install, administer, and maintain your Raspberry Pi
  • Create a network fileserver for sharing documents, music, and videos.
  • Host a web portal, collaboration wiki, or even your own wireless access point.
  • Connect to your desktop remotely, with minimum hassle.

In Detail

The Raspberry Pi is more than just a platform for teaching students how to program computers! The recipes in this book show you how this inexpensive computer can be used out of the box for a number of practical solutions that utilize existing networks and connectivity.

The Raspberry Pi Networking Cookbook is an essential reference full of practical solutions for use both at home and in the office. Beginning with step-by-step instructions for installation and configuration, this book can either be read from cover to cover or treated as an essential reference companion to your Raspberry Pi.

Full of practical and engaging content designed to expand and build upon your existing skills as you work through individual recipes, any computer novice can quickly learn how to become a Raspberry Pi expert without any programming knowledge required. The Raspberry Pi Networking Cookbook will allow you to revolutionize how you use technology on a daily basis, ranging from sharing your media across multiple devices to deploying your very own web portal, or even accessing your desktop remotely.

What you will learn from this book

  • Get started with the Internet of Things (IoT).
  • Discover how to configure and secure your Raspberry Pi device.
  • Enable remote access both to and from other computers.
  • Use your Raspberry Pi to securely share your documents and files.
  • Learn how to deploy a web server capable of serving your own content.
  • Build your own wireless access point and even a firewall.

Approach

Written in an accessible yet practical manner, the “Raspberry Pi Networking Cookbook” is the perfect companion guide for the ARM GNU/Linux box. From the moment you get your hands on your Raspberry Pi you can start to build your understanding with our specially selected collection of recipes.

Who this book is written for

This book is for anybody who wants to learn how they can utilize the Raspberry Pi to its full potential without having to immediately dive into programming. It’s full of step-by-step instructions and detailed descriptions in language that is appropriate for computer enthusiasts and experts alike.

Table of Contents

Chapter 1: Installation and Setup
Chapter 2 : Administration
Chapter 3 : Maintenance
Chapter 4 : File Sharing
Chapter 5 : Advanced Networking

Book Details

  • Author:
  • Pages: 204 pages
  • Edition: 1
  • Publication Date: 2013-03-07
  • Publisher:
  • Language: English
  • ISBN-10: 1849694605
  • ISBN-13: 9781849694605

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