Data Analysis in Research

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Data Analysis in Research

Data analysis is a method of obtaining, converting, refining and modelling data to make meaningful inferences from data. The outcomes so acquired are described, proposing results, and strengthening decision-making. In our routine life, we come across information through print, audio and visual media, social gatherings, and discussions. Data analysis is defined as a cleaning process, processing, and modelling of data to discover useful information for business decisions.

Data analysis helps in extracting useful information from the given data and making decisions. Data mining is part of the analysis. This is when the analyst explores data to discover any model or trend. The result of the descriptive analysis is a visual representation of the data, such as a bar chart, for example, or a pie chart. Thus, descriptive analyses condemn large volumes of data in a clear and simple description of what happened. This is often the starting point for a deeper analysis since we now explore.

Types of Data Analysis

Data analysis is a process of examining, transforming and modelling data with an aim of extracting useful information and conclusions and supporting decision making.

In data analysis, there are four major analysis types listed as follows:

Descriptive Analysis

The descriptive analysis analyses what happened in the past. As its name suggests, the purpose of descriptive analysis is simply to describe what happened, do not try to explain why this could be successful or establish causal relationships. The goal is only to provide an easily digestible snapshot. For example, Google Analytics helps in descriptive analysis. It provides a simple description of how many people visited a website in a given time and many other details. Similarly, tools such as HubSpot will show you how many people have opened a particular e-mail or have been dedicated to a certain campaign.

Diagnostic Analysis

The main purpose of the diagnostic analysis is to identify and respond to anomalies in their data. To reach the root cause, the analyst will start by identifying any source of additional data that can offer more information on why the sales drop occurred. They could deepen to find it, despite a healthy volume of websites and a good number of shares “Add to cart”, very few customers truly proceeded to realise and make a purchase.

After an additional inspection, it is light that most customers have abandoned the ship to the point of completing their delivery address. Now, you are going somewhere! Is starting to seem that there was a problem with the address form; Perhaps it is not loaded correctly on the mobile phone, or it was simply too long and frustrating. With a small excavation, you are closer to find an explanation for your data anomalies.

Predictive Analysis

Predictive analysis tries to predict what could happen in the future. Based on past models and trends, data analysts can design predictive models that value the probability of an event or future result. This is particularly useful since it allows companies to plan.

Prescriptive Analytics

Provides guidance on what should happen next. In other words, it examines data or content to answer the question “What should be done? The prescriptive analysis is, without doubt, the more complex type of analysis, which involves algorithms, machine learning, statistical methods and modelling procedures of the computer.

This allows you to see how each combination of conditions and decisions may affect the future and allows you to measure the impact that a particular decision might have. Based on all the possible scenarios and potential outcomes, the company can decide which is the best “ route “ or action to be taken.

Techniques and Methods of Data Analysis

Depending on the era and your enterprise goals, you could pick from some distinct statistics evaluation techniques. Some important methods of data analysis are:

Text Analysis

This is likewise referred to as statistics mining. Text evaluation makes use of databases or statistics mining equipment to locate styles inside huge statistics units. This way that evaluation will remodel uncooked statistics into enterprise insights. With the cap potential to find styles in huge units of statistics, you could then make higher decisions.

Statistical Analysis

With beyond statistics displayed within the dashboard, the statistical evaluation solutions the query of “What happened?”

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