Preparing Data for Analysis with JMP

Access and clean up data easily using JMP®!

Data acquisition and preparation commonly consume approximately 75% of the effort and time of total data analysis. JMP provides many visual, intuitive, and even innovative data-preparation capabilities that enable you to make the most of your organization’s data.

Preparing Data for Analysis with JMP® is organized within a framework of statistical investigations and model-building and illustrates the new data-handling features in JMP, such as the Query Builder. Useful to students and programmers with little or no JMP experience, or those looking to learn the new data-management features and techniques, it uses a practical approach to getting started with plenty of examples. Using step-by-step demonstrations and screenshots, this book walks you through the most commonly used data-management techniques that also include lots of tips on how to avoid common problems.

With this book, you will learn how to:

• Manage database operations using the JMP Query Builder
• Get data into JMP from other formats, such as Excel, csv, SAS, HTML, JSON, and the web
• Identify and avoid problems with the help of JMP’s visual and automated data-exploration tools
• Consolidate data from multiple sources with Query Builder for tables
• Deal with common issues and repairs that include the following tasks:
  • reshaping tables (stack/unstack)
  • managing missing data with techniques such as imputation and Principal Components Analysis
  • cleaning and correcting dirty data
  • computing new variables
  • transforming variables for modelling
  • reconciling time and date
• Subset and filter your data
• Save data tables for exchange with other platforms

Table of Contents

Chapter 1. Data Management in the Analytics Process
Chapter 2. Data Management Foundations
Chapter 3. Sources of Data and Their Challenges
Chapter 4. Single Files
Chapter 5. Database Queries
Chapter 6. Importing Data from Websites
Chapter 7. Reshaping a Data Table
Chapter 8. Joining, Subsetting, and Filtering
Chapter 9. Data Exploration: Visual and Automated Tools to Detect Problems
Chapter 10. Missing Data Strategies
Chapter 11. Data Preparation for Analysis
Chapter 12. Exporting Work to Other Platforms