What is Data analysis? (Types & Methods)

Data analysis

Data analysis is an exciting field with excellent career opportunities. However, there are misconceptions revolving around the individuals that you should dispel. However, the misconception is that you don’t have to be a math/computer science/coding whiz to learn data analysis or get a job.

But how do you know if data analysis is something that interests you? For that, we have compiled this detailed article on the meaning, types, and data analysis methods. Alternatively, you can enroll for the Data Analysis Training in Noida for a more advanced approach to entering this industry.

Data Analysis: Meaning

Although many groups, organizations, and experts approach data analysis in distinct ways, you can extract the majority of them into a one-size-fits-all definition. It is the process of cleaning, transforming, and processing raw information to extract actionable, relevant information that assists businesses in making informed decisions.

It entails sifting through massive amounts of unstructured data and extracting key insights from it. These insights are beneficial for decision-making in businesses of all sizes.

Types of Data Analysis

In order to know the objectives of data analysis, it is necessary to understand its various types. These types are as follows:

● Descriptive Analysis

Descriptive analytics helps to provide an answer to the question, “What happened?” However, the goal of this data analysis is to summarise data in a comprehensible and descriptive way rather than to make predictions. The two crucial examples of descriptive analysis are monthly revenue reports and KPI dashboards.

● Exploratory Analysis

The exploratory analysis delves a little deeper than descriptive analytics. It looks for noticeable patterns and trends in data. Another way to think about it is the preliminary investigation phase.

● Diagnostic Analysis

Investigates the findings from descriptive and exploratory analytics further to determine the causes.

● Predictive analysis

The majority of data Scientists use this type of analysis more than information analysts. It employs data, statistics, and machine learning algorithms and techniques to predict future outcomes based on data. However, sales forecasting and risk assessment are two examples of predictive analysis.

● Prescriptive Analysis

It uses insights from various types of data analysis (descriptive, exploratory, diagnostic, and predictive) to determine the best course of action.

Data Analysis Methods

What methods do data analysts employ to achieve the various goals? As a result, here’s a quick look at data analytics methods.

● Cluster Analysis

It is the act of gathering a set of data elements in a way that the components are more similar to those in other groups. That’s the reason it is known as the term cluster. Because clustering has no target variable, you can frequently use it to discover hidden patterns in data. Also, you can use the method to add context to a trend or dataset.

● Cohort Analysis

This information analysis method examines and compares a specific segment of user behavior, which you can then group with others with similar characteristics. However, this methodology can provide a wealth of insight into consumer needs or a solid understanding of a larger target group.

Moreover, cohort analysis is beneficial in marketing analysis because it allows you to understand the impact of your campaigns on specific groups of customers.

● Regression Analysis

Regression analysis uses historical information to determine how one (linear regression) or more independent variables (multiple regression) impact the value of a dependent variable. Thus, understanding the link between each variable and how it evolved in the past allows you to predict alternative outcomes and make better decisions in the future.

● Factor Analysis

Factor analysis, commonly known as “dimension reduction,” is a method of analysis that describes variability among associated variables in terms of a possibly smaller number of unobserved variables known as factors. Here, the goal is to find independent latent variables, which are excellent for simplifying certain parts. However, a customer review of a product is the best approach to grasp this data analysis process.

● Data Mining

This strategy encompasses engineering metrics and insights for added value, direction, and context. However, data mining uses exploratory statistical assessment to find relationships, linkages, patterns, and trends to develop enhanced knowledge. Moreover, adopting a data mining approach is crucial to success when evaluating how to analyze data, as it is a subject worth examining more.

● Text Analysis

Text analysis, often known as text mining in the business, works by collecting enormous amounts of textual data and organizing it in a way that makes it easier to handle. Also, you must extract the relevant data for your company and utilize it to produce meaningful insights that will move you ahead if you work through this purification process in great detail.


Hopefully, you may find this article informative. We have compiled the different types and methods of data analysis. Looking at these methods and types of data analysis, you may decide if this domain is suitable for you. Alternatively, you can enroll in the Data Analysis Training in Delhi for a more advanced approach as it’s the easiest way to enter the information analysis industry.



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