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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.

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