Introduction


Today, we reside in an era that can be addressed as a 'data-rich age'. The call of the modern era is to understand the process of analyzing and extracting digital insights. You might have a question  'how to extract data from the huge volume of data created daily?' It's possible by learning the methods of data analysis in research.

In this article, you will explore the basics of analyzing data in the real world. You will also come across a few data analysis tools that help execute data analysis methods and techniques.
Now, before we dive deep into the details of the data analysis methods, we shall foremost have a clear understanding of what data analysis is.

What is Data Analysis?


The methodology of collecting, copying, and analyzing data is known as data analysis. Data analysis is done to draw out insights that support decision-making. There are many unique methods to perform analysis. The choice of the particular data analysis technique depends on the industry and the aim of the investigation.

Data analysis can be also understood as modeling & processing raw data for extracting actionable and applicable information that helps businesses make informed decisions. Data analysis is helpful to cut off the risks associated with decision-making. How? By distributing useful insights and statistics in the form of charts, images, tables, and graphs. Data analysis has a huge contribution to processing big data into useful information.

What are the types of data analysis methods?


Talking the types of data analysis methods, they are broadly classified into 5 types as follows:

1. Descriptive Analysis


Descriptive analysis is the 1st and most important type for conducting statistical analysis. Here, one gets informed about the distribution of data. Descriptive analysis helps observe outliers & pinpoint variable associations. This type of analysis is carried out with a huge set of data where data is broken down into two categories; i.e., descriptive analysis for each variable and combinations of variables.

2. Regression Analysis


Regression analysis is considered a presiding data analysis technique used in the industry on a huge scale. This is a technique that involves identifying the relationship between two or more variables of interest. For this, one has to plot the data on a chart.

3. Dispersion Analysis


Dispersion is another type of data analysis where distribution is stretched or squeezed. Mathematically, dispersion analysis is where one needs to find the difference of values among themselves. It can also be defined as identifying the difference between the average value. The dispersion is less provided the difference between the value and average is very low.

4. Factor Analysis


Factor analysis is yet another popular kind of data analysis technique. This technique is used for detecting underlying structures in a set of variables. By locating the independent variables in the data set one can describe the patterns and models of relationships.

5. Time Series


Time series analysis deals mainly with time-series data or trend analysis. You must have a question; what is time-series data? Time series data is data present within a series of distinct time intervals or periods.

What does the Data Analysis Process look like?


Now that you know the meaning of data analysis, let's move ahead with the data analysis process. The data analysis process comprises collecting all necessary information, processing & exploring the data. Once the processing is done, the final step is to detect patterns.

Below we have highlighted the 7 steps of data analysis simply.

1. Data Requirement Gathering


The 1st step is to question yourself on why you need to do this analysis. Also, include the questions  how & which data to include in the data analysis procedure.

2.  Data Identification


After gathering answers to certain questions comes the next step is  data identification. A business must identify the type of data for data analysis. Why? Because the business owner must know the customer journey framework.
Here, you have to consider your customer's touchpoints. Begin guessing the areas where you can collect the most meaningful data. You can consider the types of tools that you will use for data collection.

3.  Data Collection


After identifying data, move on to the 3rd step  data collection. You have to work hard to gather data from your sources. A few faithful sources include case studies, investigations, interviews, opinion polls, direct observation, and focus groups. Remember to organize the collected data for data analysis.

4.  Data Cleaning


After completing the 3rd step you will be left with a huge & massive amount of collected data. Fact is told, all the data you collect won't be useful, so you ought to clean it up. Data cleaning is a process where you have to eliminate general errors like white spaces & duplicate records.

5.  Data Analysis


Now, you have finally landed on the most important step  data analysis. Here, you will have to use analysis software and other tools for interpreting and understanding the data. Data analysis tools that are helpful include Excel, Python, R, Chartio, Metabase, and Microsoft Power BI.

6.  Data Interpretation


You have successfully executed data analysis & now you have your results. In this 6ts step, you have to decode the results and present the breakthrough of action.

7.  Data Visualization


This is the last and final step in the entire data analysis process. Why? Because visualization assists in driving valuable insights. Data visualization helps establish a comparison between several datasets.
Data visualization means presenting your information graphically. The data visualization must be executed such that the audience can read and understand it. Use bulletins, charts, graphs, maps, & other methods for presenting the visualized data.

How to Become a Data Analyst?


By now you must be well-aware of deep & clear data analysis. In case you are interested in building a career in data analytics, you must analyze what it takes to become a data analyst. You can do this easily by opting for online data analytics courses. You can obtain certifications and begin a career here.