Stages of data analysis

Data analysis is the process through which the content of the data is scrutinized and checked, combed to be more effective and accurate, and then reconfigured, and stored to obtain and depot information that can be relied upon in making and determining what decisions are. Data analysis can be carried out in many different ways, depending on the nature of the field in which it is used. We can use a good level of data analysis within the sciences and social sciences as well as finance.

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what are data analysis objectives?

Data analysis aims to create what is called a data model for the system. This process is considered one of the main activities related to the analysis phase, in which the data is often modeled using various graphic models, that is, the use of charts as well as drawings that are somewhat similar and on a specific principle to several charts that cause the flow of data.

process and stages of data analysis

  • first stage: Determining the data requirements
  • It is the first step in conducting the data analysis process and it is intended to define the data analysis quantity as well as the quantity of data and other important and necessary things that are required to be available in the content of the data to be analyzed, for example: Are the required data numbers, Or is it text or images, will this data be calculated for one person or is this data for all people in this place..etc. for many requirements.

  • second stage: collecting the data to be analyzed
  • In this stage, data is collected from many different sources so that it fulfills the requirements called for It may be collected by many people, .in the first step or it may be obtained through many modern technologies such as satellites, traffic lights, or tthrough the Internet...etc.

  • third stage: Organizing the data to facilitate analysis
  • After completing the data collection stage, the process of distributing the data begins in the form of tables consisting of rows and columns, as is the case in Excel.

  • Fourth stage: Examining the data to be analyzed
  • It is necessary and very important that the data be examined to ensure that the resulting information does not contain errors or that those data are This is done by checking by reviewing the .incorrect data and working to remove or correct the Also, the wrong data may be incorrect .erroneous numbers, duplicate data, or salary data but contains It is possible to get rid of the .alphabetic characters content of false data by removing the duplicate and then recalculating the numbers. During the process of entering the data, it is checked and ensured that the data entered have the same type for the same column format.

  • Fifth stage: preparing and preparing the conceactual model for the data
  • This step is also called the process of modeling the Through it, the model is built that .system data reflects and shows the content of the main topics (things) related to the data and clarifies the extent of Analysis at this .their relationships with each other level is called content analysis or is called meaning analysis.

  • Sixth stage: Relationships analysis
  • In this stage, work is done to improve the conceptual model by redesigning the entities in a way that reduces the occurrence of repetitions and transforms the entities into a set of simplified relationships that can be dealt with smoothly, This process is also called the .flexibly, and easily process of normalization or data normalization and building the methodology and mechanism of the relational model of data.

  • Seventh stage: Database design
  • This stage is concerned with transforming the model for clarifying relationships into a description and clarification within the system database.

Data analysis is used to evaluate data with statistical tools to discover useful information. A variety of methods are used including data mining, text analytics, business intelligence, combining data sets, and data visualization.

Programming languages used in data analysis

There are two leading languages in the field of data analysis, Python and R, and they are considered the appropriate and best languages for intelligent data analysis. They are among the easiest languages that can be learned, as they do not require any effort and time to learn, nor any prior knowledge of programming.

The advantages of data are many, but you can’t access these benefits without the proper data analytics tools So we introduceAnalyzeCloud a user-friendly platform that makes you able to extract your data from any data source and get insights without any coding or complicated steps.

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