Lecture note Data visualization - Chapter 31

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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.

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Nội dung Text: Lecture note Data visualization - Chapter 31

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
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,
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
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
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 softwarebased information visualization adds building blocks for interacting with and representing various kinds of abstract data
Continued…. Process of data visualization Acquire Parse Filter Mine Represent Refine Interact
Acquire Obtain the data, whether from a file on a disk or a source over a network Parse Provide some structure for the data’s meaning, and order it into categories Filter Remove all but the data of interest Mine Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context
Represent Choose a basic visual model, such as a bar graph, list, or tree. Refine Improve the basic representation to make it clearer and more visually engaging. Interact Add methods for manipulating the data or controlling what features are visible.
Continued…. Iteration and Combination of steps of data visualization Unique requirements for each project each data set is different the point of visualization is to expose that fascinating aspect of the data and make it selfevident readily available representation toolkits are useful starting points they must be customized during an indepth study of the task
Continued…. Avoid usage of excess data Audience of problem Quantitative messages TimeSeries Ranking ParttoWhole Deviation FrequencyDistribution Correlation
Timeseries: A single variable is captured over a period of time, such as the unemployment rate over a 10year period. A line chart may be used to demonstrate the trend Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance by sales persons during a single period A bar chart may be used to show the comparison across the sales persons
Parttowhole: Categorical subdivisions are measured as a ratio to the whole A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market Deviation: Categorical subdivisions are compared again a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period A bar chart can show comparison of the actual versus the reference amount
Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 010%, 11 20%, etc. A histogram, a type of bar chart, may be used for this analysis A boxplot helps visualize key statistics about the distribution, such as mean, median, quartiles, etc. Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions
Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code A bar chart may be used for this comparison Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building A cartogram is a typical graphic used
Continued…. Characteristics of effective graphical display show the data avoid distorting what the data have to say present many numbers in a small space make large data sets coherent encourage the eye to compare different pieces of data reveal the data at several levels of detail, from a broad overview to the fine structure
Continued…. Visual perception and data visualization Effective graphics take advantage of preattentive processing and attributes and the relative strength of these attributes Types of information display Tables Graphs Data display requires planning Data collection
Benefits of data visualization Visualization is so powerful and effective that it can change someone’s mind in a flash it encompasses various dataset quickly, effectively and efficiently and makes it accessible to the interested viewers It motivates us to a deep insight with quick access  It gives us opportunity to approach huge data and makes it easily comprehensible, be it the field of entertainment, current affairs, financial issues or political affairs It also builds in us a deep insight, prompting us to take a good decision and an immediate action if needed It has emerged in the business world lately as geospatial visualization
Data Visualization with C++ Chapter 1 “Arrays, Pointers and Structures” Chapter 2 “Objects and Classes” Chapter 4 “Inheritance” Chapter 6 “Algorithm Analysis”