tailieunhanh - Lecture note Data visualization - Chapter 31

In this lecture we learned about: Definition of data visualization, terms related to data visualization, data mining, data recovery, data redundancy, data acquisition, data validation, data integrity, data verification, data aggregation. | Lecture 31 Introduction to Data Visualization Definition of Data Visualization Terms related to Data Visualization Data Mining Data Recovery Data Redundancy Data Acquisition Data Validation Data Integrity Data Verification Data Aggregation Continued . Data mining analytic process designed to explore data analyzing data from different perspectives summarizing it into useful information Data recovery handling the data through the data from damaged, failed, corrupted, or inaccessible secondary storage media recovery required due to physical damage to the storage device or logical damage to the file system Continued . Data redundancy additional to the actual data permits correction of errors Data acquisition process of sampling signals measure real world physical conditions converting the resulting samples into digital numeric values Data validation process of ensuring that a program operates on clean, correct and useful data Continued . Data integrity maintaining and assuring the . | Lecture 31 Introduction to Data Visualization Definition of Data Visualization Terms related to Data Visualization Data Mining Data Recovery Data Redundancy Data Acquisition Data Validation Data Integrity Data Verification Data Aggregation Continued . Data mining analytic process designed to explore data analyzing data from different perspectives summarizing it into useful information Data recovery handling the data through the data from damaged, failed, corrupted, or inaccessible secondary storage media recovery required due to physical damage to the storage device or logical damage to the file system Continued . Data redundancy additional to the actual data permits correction of errors Data acquisition process of sampling signals measure real world physical conditions converting the resulting samples into digital numeric values Data validation process of ensuring that a program operates on clean, correct and useful data Continued . Data integrity maintaining and assuring the accuracy and consistency of data ensure data is recorded exactly as intended Data verification different types of data are checked for accuracy and inconsistencies after data migration is done Data aggregation information is gathered and expressed in a summary form to get more information about particular groups Continued . Need for data visualization Importance of data visualization Limitation of spreadsheet Interpretation through data visualization identify areas that need attention or improvement understand what factors influence design system predict how to change system design accordingly predict the efficiency of system Interactive Visualization Humans interact with computers to create graphic illustrations of information Process can be made more efficient Human input Response time Continued . Combination of disciplines data visualization to provide a meaningful solution requires insights from diverse fields like statistics, data mining, graphic design, and information visualization .