Data analysis is the process of cleaning, transforming, and modeling data to discover some useful information that could facilitate decision-making. Thus, the goal of data analysis is to get some useful information from the data and then make decisions based on the information gathered.

Data analysis, in its simplest form, is applied in our daily decision-making. Before any decision, making process, we often analyze what happened in the past. This is data analysis where the memories are the data. Business analysts copy the same idea. In today’s world, data analysis has become an integral part of the business decision-making process.

In statistical dialect, data analysis can be divided into three categories- exploratory data analysis, confirmatory data analysis, and descriptive data analysis. Exploratory data analysis focuses on discovering new features of the data. Confirmatory data analysis involves the use of a hypothesis. It could either reject or accept a hypothesis. Descriptive statistics: as the name suggests, gives a general idea about the data. Examples of descriptive statistics include the mean, variance, and standard deviation.

The process of data analysis

In general, analysis means breaking down something into its individual component for examination. The data analysis process, which is commonly applied by a data analysis tutor, involves the following iterative steps.

Data requirements specification

For data analysis to be carried out, there must be a specific question to be answered. For example, a business might be experiencing losses selling a certain product and wants to increase its sales. It is important to note there is a lot of data out there. Large institutions and government might have the data, but the main problem would be getting the data suited to the problem at hand. Therefore, we have to be careful with the specific variables that we use in data analysis.

Data collection

Once we are clear with the data requirement, the next process is gathering the data. There are so many ways of collecting data. If found suitable, traditional methods such as surveys, observation, or the use of questionnaires could be employed. Web scraping is another method used to collect data form the web. It could be manual or automated. Whatever method is used, it’s important to ensure the accuracy of the data and honesty.

Data processing

If the data collected is not in its final form then we can say, it still not suitable for analysis. Here we can structure the data for analysis using tools. For instance, we could store it in columns and rows.

Data cleaning

This is the final step to ensure that the data is ready for analysis. In some cases, the data might contain missing values, duplicates, or other forms of undesirable errors. Data cleaning is the process of correcting these errors. It involves removing values within or below a certain threshold. The method of data cleaning applied is largely dependent on the data. If it is quantitative, then there are quantitative methods for outlier detection. For textual data, data cleaning is used to remove the mistyped words. The accuracy of removing mistyped words is still questionable.

Data analysis

Now we have the data that is ready for analysis. Various data analysis techniques can be used to understand, interpret, and derive conclusions about the data. Data visualization is a method that can be used to get an insight into the data using the various plots. Still, other statistical models can be applied, such as correlation, and regression.

Communication

In this stage, the results of the data analysis process are reported to the user or the management of a company.

Data analysis assignment help.

Data analysis has advanced with technology; there are so many types of software used for the data analysis process. Computer programming languages such as python have been implemented in data analysis while there is various statistical software such as R, Matlab, and Excel.

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