Page 128 - Ai_V1.0_Class9
P. 128
Data Exploration
After collecting accurate data, the next step is data exploration. Data exploration means finding the patterns
and trends in the data. It is the third stage in the AI project cycle and the initial step in data analysis. It is used to
understand what is in a dataset and the characteristics of the data.
Data exploration cleans the big data to provide an input to an AI project. Terabytes of data sitting in the data centre
unused is a burden, if correctly processed it can become digital gold.
Data Visualisation
Data visualisation is the graphical representation of data and information. By using graphical tools like charts and
graphs, it is an easier way to understand the trends and patterns in data.
Data visualisation can be done through a combination of automated tools and manual methods. It encompasses
all aspects of the visual representation. It helps you arrange your scattered data into a structured pattern with
specific characteristics following important trends in detail. It also helps to make better decisions.
Why do you think we need to explore and visualise data before jumping into the AI model? The following are some
reasons:
• Understand trends: Visual representation of data grabs our interest and keeps our concentration. If we see
data in the form of numbers, understanding it will take some time. But when we see the same data in the form
of a chart, we quickly see trends and outliers.
• Deciding which model: Understanding visuals by humans is better than any tabular data format or reports.
Data visualisation tools accelerate decision-making based on the data insights, accelerating business growths.
• Easier to comprehend: Visualisation is a key tool to make sense of huge data in the form of rows. A good
visualisation tool helps to clean big data and highlight useful information. It enables decision-makers to
interrelate the data to find better insights and be easy to comprehend.
• Easier to communicate: Visualisation lets you communicate large amounts of data to the audience with ease.
Interactive data visualisation tools help to communicate data findings and critical information effectively.
Need of Visualising Data
The needs for data visualisation are:
• It simplifies the complex quantitative information.
• It analyses and explores big data easily.
• It identifies the areas for improvement.
• It identifies the relationship between data points and variables.
• It explores new patterns and reveals hidden patterns.
Data Visualisation Tools
There are both manual analysis and automated analysis tools available for data visualisation. These tools help
us to visually explore and identify relationships between different datasets. Some of the commonly used data
visualisation tools are:
• Microsoft Excel: It is a manual data exploration tool provided by Microsoft. It provides different types of basic
charts and objects for visualising data. However, it is limited to use only for representing small amounts of
organised datasets. It is not preferable for big data.
• Tableau Public: It is free software allows us to create interactive data visualisations that we can share with
others. It has very large datasets. It is one of the most commonly used data visualisation tools.
126 Artificial Intelligence Play (Ver 1.0)-IX

