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Similar to Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations.
Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations.
- 1. DATA VISUALIZATION NAME: SAMARPITNANDANWAR ROLL NO. : 48 DEPARTMENT : CSE GUIDED BY : PROF. P.D. KAWARE HVPM COET A Presentation
- 2. CONTENT • Introduction • Whatis Data? • What is Visualization? • What is Data Visualization? • Categories of Data Visualization • Types of Data Visualization • Visualization Tools • Data Visualization Best Practices • 7 Steps in Data Visualization • Example of Data Visualization • Benefits • Drawbacks • Conclusion
- 3. INTRODUCTION • Data Visualizationis a Graphical Representation of any data or information. • Visual Elements such as Charts, Graphs, and Maps are few Data Visualization Tools that provide the user to understand the data. • In this world of Big Data, Data Visualization helps you to make decision, study the Data properly and Understand the concepts properly. • Why is Data Visualization Important : • Easily Graspable • Establish Relationships • Interactive Visualization
- 4. WHAT IS DATA? • Data is a collection of information gathered by observations, measurements, research or analysis. • Data is organized in the form of graphs, charts or tables. • Data can be of many types :- • Video • Music • Images, etc.
- 5. WHAT IS VISUALIZATION? • Visualization is any technique for creating images, diagrams, or animations to communicate a message. • Use bar charts to compare items between different groups, measure changes over time and identify patterns or trends.
- 6. WHAT IS DATAVISUALIZATION ? • Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics, and even animations. • Visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand. • Data Visualization is categorize into four key purposes :- • Idea generation • Idea illustration • Visual discovery • Data Visualization
- 7. IDEA GENERATION : •Idea Generation is the act of forming ideas. • Data visualization is commonly used to encourage idea generation across teams. • They are frequently manipulated during Design Thinking sessions at the start of a project.
- 8. IDEA ILLUSTRATION : •A drawing, diagram or picture in a book or magazine. • Data Visualization for Idea illustration help to make ideas, thoughts, feelings, etc. known to somebody. • It is used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. • Project managers frequently use Gantt charts and waterfall charts to illustrate workflows.
- 9. VISUAL DISCOVERY : •Visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset. • Visual discovery and every day data viz are more closely aligned with data teams.
- 10. DATA VISUALIZATION : •Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers.
- 11. TYPES OF DATAVISUALIZATION Tables Pie Charts Line Charts Histograms Scatter Plots Tree Maps
- 12. TABLES • This consistsof rows and columns used to compare variables. • Tables can show information in a structured way. • Tables can be a disadvantage for users which are looking for high level trends.
- 13. PIE CHARTS • Thesegraphs are divided into sections that represent parts of a whole. • They provide a simple way to organize data and compare the size of each component to one other.
- 14. LINE CHARTS • Thesevisuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. • Line graphs utilize lines to demonstrate these changes.
- 15. HISTOGRAMS • This graphplots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. • This visual makes it easy for an end user to identify outliers within a given dataset.
- 16. SCATTER PLOTS • Thesevisuals are beneficial in revealing the relationship between two variables, and they are commonly used within regression data analysis. • These can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.
- 17. TREE MAPS • Displayhierarchical data as a set of nested shapes, typically rectangles. • Tree maps are great for comparing the proportions between categories via their area size.
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- 19. DATA VISUALIZATION BESTPRACTICES Choose an effective visual Keep it Simple Know your Audiences
- 20. 7 STEPS INDATA VISUALIZATION Develop your research question Create your data Clean your data Choose a Chart Type Choose your tool Prepare Data Create Chart
- 21. EXAMPLE : WORLDPOPULATION AT 8 BILLION
- 22. BENEFITS OF DATAVISUALIZATION • Enhanced Understanding • Improved Decision-Making • Identifying Patterns and Trends • Increased Efficiency: • Effective Communication:
- 23. DRAWBACKS OF DATAVISUALIZATION • Limited Context • Data Quality and Integrity • Time and Skill Requirements • Data Security and Privacy Concerns
- 24. CONCLUSION • Data Visualizationis an important practice that allows for clear and effective communication of data through use of graphics. • Makes Data understandable and support decision making. • Allows Decision makers to identify patterns and trends in data.
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