Data analysis is a method of collecting, transforming, cleaning, and modeling data with the purpose of discovering the specified data. The results so obtained are communicated, suggesting conclusions, and assisting choice-making. Data visualization is at instances used to portray the data for the convenience of discovering the useful patterns inside the data. The terms information modeling and data analysis mean the same.
Data analysis method consists of the following levels which are iterative in nature –
The information required for analysis is based on a question or a test. Based at the requirements of those directing the analysis, the data essential as inputs to the analysis is recognized (e.g., population of people). precise variables regarding a population (e.g., Age and income) can be unique and obtained. data can be numerical or specific.
Data collection is the technique of collecting information on focused variables identified as data necessities. The emphasis is on ensuring correct and honest collection of data. Information series ensures that data collected is correct such that the related choices are valid. Data collection presents both a baseline to measure and a target to improve.
Data is collected from numerous assets starting from organizational databases to the information in internet pages. The data therefore received, may not be based and may contain irrelevant data. Therefore, the collected data is required to be subjected to information Processing and data cleaning.
The data that is collected want to be processed or prepared for analysis. This consists of structuring the information as required for the relevant analysis tools. for instance, the data may have to be located into rows and columns in a table within a Spreadsheet or Statistical application. A data version might have to be created.
The processed and prepared data may be incomplete, contain duplicates, or contain errors. data cleaning is the method of preventing and correcting those errors. There are several types of data cleaning that depend on the type of data. for example, while cleaning the financial data, certain totals might be compared against reliable published numbers or defined thresholds. Likewise, quantitative data techniques can be used for outlier detection that would be subsequently excluded in analysis.
Data that is processed, prepared and cleaned could be prepared for the analysis. Numerous data analysis strategies are available to recognize, interpret, and derive conclusions based at the requirements. Information Visualization may also be used to study the data in graphical layout, to gain extra perception regarding the messages within the information.
Statistical data models which include Correlation, Regression analysis may be used to identify the relations among the records variables. These models which are descriptive of the data are useful in simplifying evaluation and communicate results.
The technique might require extra information cleaning or extra data series, and hence those activities are iterative in nature.
The consequences of the data analysis are to be mentioned in a format as required through the users to assist their decisions and further action. The feedback from the users might result in extra analysis.
The data analysts can select information visualization strategies, which include tables and charts, which assist in communicating the message really and efficiently to the users. The analysis tools offer facility to highlight the specified information with color codes and formatting in tables and charts.
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