Data Exploration | Artificial Intelligence for Class 10 PDF Download

What is data exploration?

Data Exploration involves techniques and tools used to visualize data through advanced statistical methods.

Advantages of Data Visualization

• Enhances understanding of data.
• Provides insights into the data.
• Allows user interaction.
• Enables real-time analysis.
• Assists in decision-making.
• Simplifies data complexity.
• Reveals relationships and patterns within the data.
• Helps define a strategy for your data model.
• Facilitates effective communication among users.

Up to this point, you have covered problem scoping and data acquisition. You’ve established your goal for the AI project and identified methods for acquiring data. However, the main issue with the acquired data is its complexity due to numerical values. To effectively use these numbers, you need a specific pattern to interpret the data.
For instance, when choosing a book in a library, you might skim through the pages to review and select the one you prefer. Similarly, when working with or analyzing data, data visualization is essential.

Data Visualization Tools

There are numerous data visualization tools available, and their number is continually growing. In the next section on Data Exploration AI, we will discuss these tools.
Here is a list of 20 data visualization tools for your reference:

• Microsoft Excel
• Tableau
• QlikView
• FusionCharts
• DataWrapper
• MS Power BI
• Google Data Studio
• Sisense
• HiCharts
• Xplenty
• HubSpot
• Whatagraph
• Adaptive Discovery
• Teammate Analytics
• Jupyter
• Dundas BI
• Infogram
• Google Charts
• Visme
• Domo

Do some research to learn how to visualize your data using these tools.

How to select a proper graph?

Now that you’re familiar with various chart types, the next step is to choose the most suitable chart for data visualization. The selection depends on the data and the objective you aim to achieve with your model.
Here are some basic chart purposes to help you choose an appropriate chart:

• Comparison of Values: To show periodical changes, use a Bar Chart.
• Comparison of Trends: To display changes over time, use a Line Chart.
• Distribution of Data by Categories: To show data by category, use a Histogram.
• Highlight a Portion of a Whole: To emphasize data based on value, use a Pie Chart.
• Show Relationships Between Data: Use multiple charts to illustrate relationships between data sets.