Data Processing| Practical Work in Geography Class 12

Geography Class 12 Notes Chapter 2 talks about Data Processing. The chapter is dedicated to discussing the different types of operations including transformation, analysis, and organizing the raw data sources into a particular useful format. The processing of data may include the collecting, processing, and sorting of those raw data in a readable format.

In this article, we are going to discuss Geography Class 12 Chapter 2 which is Data Processing for the practical works in detail.

Data Processing is a particular process that collects and manipulates the raw data source by using different algorithms and computers. The process is generally focused on converting the data into a machine-level format to calculate the output. The processing of the data is dependent on the different types of stages like decision-making and forecasting processes.

Data Processing makes and depicts the raw data sources in a comprehensive format that is a statistical way of analyzing the data. The measures of the processing of the data provide a value by sharing the representative observations by validating the data. Here are some major measurements of the Data Processing as mentioned below.

• Measurement Of Central Tendency: It finds out the core tendencies of the data sets that drive the whole data source in a data pool.
• Measurement Of Dispersion: It is a process where the common measurement processes are applied to a whole dataset to find out the core observation of the process.
• Measurement Of Relationships: In this segment, the process tries to find out the internal relations between the different data and their segments to filter them for further processes.

Here are the major terms related to the data processing as mentioned below.

• Mean: It is the value that adds all values together in a place by dividing the observation numbers.
• Median: It is a term that denotes a point. A median is a bounded and organized series that can be divided into two different halves. The median is always dependent on the actual value.
• The Mode: The mode is a common value in the whole data that can be found in the whole datasheet.
• Dispersion: Dispersion is a central tendency. It is a metric value that determines how the individual items fluctuate and are scattered around the average value.
• The Range: It is a major gap between the biggest and lowest values in a data distribution process. It particularly highlights the major distance between the high and low scores in a data pool.
• Quartile Deviation: Quartile Deviation particularly measures the absolute dispersion. It works in a broad range by ignoring the tail observation. It helps to compute the data in a matrix as a deviation.
• Mean Deviation: Mean Deviation is an average value that evaluates the absolute differences between the data set and its mean value. The Mean Deviation can be affected by so many factors like arithmetic medium, the process of median, and the working modes.
• Standard Deviation: Standard Deviation is a process that is a form of metric dispersion. It is a square root of the average which helps to measure the percentage of the mean.

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What is data processing and types of data processing?

Data processing refers to the rules by which raw data is converted into useful information. A data processing system is an application optimized for a specific type of data processing. For instance, a timesharing system is designed to run timesharing processing optimally. You can use it to run batch processing, too.

What is data cycle?

A data lifecycle refers to the different stages a unit of data undergoes, from initial collection to when it’s no longer considered useful and deleted. It’s a continuous, policy-based process where each phase informs the next.

What are the two main types of data explain?

The words are similar and easy to mix up, but an easy way to remember the difference between the two types of data is with a word trick – “quantitative” has nott, like the “quantity.” When we think of quantities, we think of numbers. “Qualitative” measures the “quality” rather than the numerical value.

What are the characteristics of data processing?

The main characteristics of data processing are: Accuracy: Data processing must be accurate in order to be useful. Any errors in the data can lead to incorrect decisions and missed opportunities. Completeness: Data processing must be complete in order to be useful.

What are the objectives of data processing?

The primary objective of data processing is to enhance the value of the information and make it easier for decision-making. It enables businesses to streamline their operations and make strategic decisions promptly.

What is data accuracy?

Data accuracy refers to the correctness and reliability of data. Accurate data correctly represents the real-world scenario or event it is supposed to depict. It’s free from errors, especially those that occur due to incorrect data entry or faulty processes.