What is Data Analysis? Research | Types | Methods | Techniques
When it comes to data analysis, it is defined as the process of cleaning, transforming, and modelling data in order to discover information that can be used for business decision-making. The purpose of data analysis is to extract useful information from data and to make decisions based on the information derived from the data analyses.
To give a simple example of data analysis, whenever we make a decision in our daily lives, we consider the consequences of that decision by considering what happened previously or what will happen if we make that particular decision. This is nothing more than a process of delving into our past or future and making decisions based on our findings. In order to do so, we must first gather memories from our past and dreams for the future. So it’s all just data analysis at this point. Data analysis is the term used to describe the same thing an analyst does for business purposes.
You will learn the following skills in this Data Science Tutorial:
Why Data Analysis?
Sometimes all you need to do to grow your business, or even to grow in your life, is conduct an analysis!
If your company is not growing, you must look back and acknowledge your mistakes, then devise a new strategy to avoid repeating the same mistakes. In addition, even if your company is expanding, you must look forward to expanding the company even further. All that is required of you is an examination of your business data and business processes.
Data Analysis Tools
Data analysis tools make it easier for users to process and manipulate data, analyse the relationships and correlations between data sets, and identify patterns and trends that can be used to interpret the data. Here is a comprehensive list of the tools that are used in research for data analysis.
Techniques and Methods for Data Analysis: Types of Data Analysis
There are many different types of Data Analysis techniques available, each with its own set of advantages and disadvantages. However, the most common Data Analysis techniques are as follows:
Types of Data Analysis: Techniques and Methods
Examining the Text
Examining the Data Statistical Analysis
Predictive Analysis is a type of analysis that predicts the future.
Prescriptive Analysis is a type of analysis that prescribes a course of action.
Text Analysis is also referred to as Data Mining in certain circles. Statistical pattern discovery is a method of data analysis that involves the use of databases or data mining tools to discover a pattern in large data sets. It was previously used to convert raw data into useful business information. Business Intelligence tools, which are available on the market and are used to make strategic business decisions, are available. Overall, it provides a method for extracting and examining data, as well as for deriving patterns and, finally, for interpreting the data.
Statistical Analysis demonstrates “What happened?” by utilising historical data presented in the form of dashboards. Statistical analysis encompasses the collection, analysis, interpretation, presentation, and modelling of data, among other activities. It is used to examine a set of data or a sample of data. Generally speaking, this type of analysis can be divided into two categories: Descriptive Analysis and Inferential Analysis.
Descriptive analysis examines a set of complete data or a sample of numerical data that has been summarised. For continuous data, it displays the mean and standard deviation, whereas for categorical data, it displays percentage and frequency.
Inferential Analysis is the process of analysing a sample of data from a larger set of data. Using this type of analysis, you can draw different conclusions from the same data by selecting different samples from the same data set.
Diagnostic Analysis answers the question “Why did it happen?” by identifying the underlying cause based on the insight gained through Statistical Analysis. This type of analysis is useful for identifying patterns in data behaviour. If a new problem arises in your business process, you can refer to this Analysis to see if there are any patterns that are similar to the new problem. It may also have opportunities to apply similar prescriptions to new problems in the future.
With the help of historical data, Predictive Analysis can show “what is likely to happen.” The most basic data analysis example is as follows: if I saved money last year and bought two dresses with it, and if my salary increases by twice as much this year, I will be able to buy four dresses. But, of course, it’s not that simple because you have to consider other factors, such as the possibility that clothing prices will rise this year, or the fact that you might prefer to purchase a new bike instead of dresses, or that you might need to purchase a house.
As a result, this Analysis makes predictions about future outcomes based on data that is currently available or that has previously been collected. Forecasting is nothing more than an educated guess. Its accuracy is determined by how much detailed information you have and how much time you spend digging through that information.
Prescriptive Analysis brings together the insights gained from all previous analyses in order to determine the best course of action to take in the context of a current problem or decision. Prescriptive Analysis is being used by the majority of data-driven companies because predictive and descriptive analysis alone are not sufficient to improve data performance. They make decisions based on current situations and problems, which they analyse and assess using data.
Data Analysis Process
In essence, the Data Analysis Process is nothing more than a process of gathering information through the use of an appropriate application or tool that allows you to explore the data and identify patterns in it. You can make decisions or reach ultimate conclusions based on the information and data you have gathered so far.
The Data Analysis process is divided into the following phases:
Data Requirement Gathering
Compilation of Data Requirements
Cleaning and Organizing of Information
Analyze the data
Data Analysis and Interpretation
Visualization of Information
Compilation of Data Requirements
First and foremost, you must consider why you want to conduct this data analysis in the first place. All you need to do is figure out what the purpose or goal of doing the data analysis is. You must first decide what type of data analysis you would like to perform! It is necessary to decide what to analyse and how to measure it during this phase. It is also necessary to understand why you are investigating and what measures you must employ in order to complete this analysis.
Following the requirement gathering process, you will have a clear understanding of the things you need to measure and the conclusions you should draw from your findings. It’s time to start gathering information based on your requirements. Once your data has been collected, keep in mind that it will need to be processed or organised in order to be used for analysis. As you gathered information from various sources, you must keep a log that includes the date the information was gathered as well as the source of the information.
Now, whatever data is collected may or may not be useful or relevant to your goal of analysis, so it should be cleaned up before being considered. It is possible that the information gathered will contain duplicate records, blank spaces, or errors. The data should be free of errors and thoroughly cleaned. This phase must be completed before the Analysis phase because, as a result of the data cleaning phase, the output of the Analysis phase will be closer to the expected outcome.
Once the data has been collected, cleaned, and processed, it is ready to be used in the analysis process. As you manipulate data, you may discover that you already have the information you require, or that you need to collect additional information. Use data analysis tools and software during this phase to better understand, interpret, and draw conclusions about the data in accordance with the requirements.
After you’ve completed your data analysis, it’s time to begin interpreting your findings. When it comes to expressing or communicating your data analysis, you have several options. You can use simple words or a table or chart, for example. Use the results of your data analysis process to determine the best course of action for your situation.
Data visualisation is very common in your day-to-day life; it can be found in the form of charts and graphs, among other forms of representation. In other words, data presented in a graphical format to make it easier for the human brain to comprehend and process. Data visualisation is frequently used to discover previously undiscovered facts and trends. Discovering meaningful information can be accomplished through the observation of relationships and the comparison of datasets.