Data Visualization & Data Exploration

What is Data Visualization?

• A method of analyzing raw data

• A technique used to represent data visually

• A process for cleaning data

• A tool for storing data

Why is Data Visualization important in business?

• It simplifies the storage of data

• It helps to improve the visual design of reports

• It allows decision-makers to understand trends and make informed choices

• It organizes data into a table

How does Data Exploration contribute to the data analysis process?

• It helps in the visualization of data

• It identifies key trends, patterns, and outliers in raw data

• It filters the data for analysis

• It stores the data for future use

What are the key benefits of using Data Visualization?

• Making the data look more colorful

• Making data easy to understand, identifying trends, and enhancing decision-making

• Storing data for future analysis

• Hiding complexity from users

What are some common types of visualizations used in Data Visualization?

• Tables and pie charts

• Pie charts, bar charts, line graphs, scatter plots, and maps

• Text reports

• None of the above

How do Data Visualization and Data Exploration work together?

• Data Visualization simplifies data storage, and Data Exploration builds databases

• Data Exploration uncovers insights, and Data Visualization presents them clearly

• They work independently, with no interaction

• Data Exploration is the same as Data Visualization

What role does interactivity play in Data Visualization?

• It allows users to adjust how data is displayed by zooming, filtering, or exploring further

• It stores data for future use

• It removes the need for a graph

• It makes visualizations static

What are the core principles of effective Data Visualization?

• Complexity and detail

• Clarity, accuracy, simplicity, and aesthetics

• Speed and size

• Color diversity

How does Data Visualization help in fraud detection in industries like finance?

• It helps analysts visualize trends in the stock market

• It helps identify unusual connections and patterns in transaction data for faster fraud detection

• It stores historical data

• It increases the volume of data

What are some tools commonly used for creating Data Visualizations?

• Power BI, Tableau, D3.js, Plotly, Google Data Studio

• Microsoft Excel and Google Sheets

• Text editors like Notepad

• Database management tools