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• Kalpesh Agrawal

# Introduction of Data Analysis

Data Analysis in the skill is which is helpful in Actuarial work, Data Science, and Business analysis. As per the survey, Data scientists have to spend around 80% of their timing cleaning and preparing for analysis.

Do you want to know about all the basic information of the Data Analysis in terms of question and answer?

Here, you will get all the answers.

### 1. What is the meaning of Data Analysis?

Data analysis is the process of collecting data in a row form, and then clean, analyze, and after the process converts it into the information which can be used for a specific purpose.

Data analysis is the process, which includes statistical and logical techniques to evaluate the data.

Data is the new oil of 21st Century, the data collected from various sources is in the form of row data, that needs to proceed to make it useful for a specific purpose, Data Analysis is the skill which enables you to do the same.

`Related: What is Data Mining & The 9 Laws of Data Mining`

### 2. Types of Data Analysis

There are four types of Data Analysis.

1. Descriptive Analysis: This is the foundation of all the data insights. Descriptive Analysis helps to describe and summarize data in a meaningful way and give the answer of the question "what happened".

2. Diagnostic Analysis: In Diagnostic Analysis, techniques like data discovery, correlation, and data mining are used to answer the question "Why did it happen".

3. Predictive Analysis: The name itself informing that this analysis predicts the future. Predictive Analysis is the use of processed data, statistical algorithms, and Machine learning techniques to predict the likelihood of certain future outcomes based on historical data.

This analysis is trying to give the answer to the question " What is likely to happen".

4. Prescriptive Analysis: Prescriptive Analysis is the opposite of Descriptive Analysis. This is the last stage of Business Analysis that uses advanced machine learning techniques and helps businesses to decide a course of action.

### 3. Process of Data Analysis

The process of Data Analysis is as below.

Specification of Data Requirement: The first step is to specify the data requirement, data may be numerical or categorical. Whether the data collected from the population or sample, by asking the questions, by survey or by experiment, specific variables must be considered regarding a population like age, income, and geography, etc.

Collection of Data: Data collection is the process of collecting the data as per specific requirements. Due to technical advancement data easily accessible fro,m the various online sources. The only problem is with the quality of the data, so that is very important to check the accuracy of the data.

The data collected from the various sources must be in the raw form and subject to Data Processing and Data Cleaning.

Processing of Data: The collected data must be put into the spreadsheet or database for analysis.

Data Cleaning: This step is the most important part of the process of Data Analysis, As per survey Data scientists spent around 80% of their time cleaning and managing data.

The data put into the spreadsheet may contain duplicate values, may be incomplete or contain errors. There are many types of Data Cleaning that depend on the type of data.

Data Analysis: To make some useful conclusions from the data, Data Analysis is an important step. After processing, organizing, and cleaning the data that would useful for the analysis.

Various techniques are used to understand, interpret, and find a conclusion from the data.

In Data Analysis some times more Data Cleaning and Data Collection may require, so these activities are constant in nature.

Modelling the data: Various types of statistical data models such as Regression Analysis, Time Series, Clustering, ANOVA, correlation many more can be used to identify the relations between the data variables.

Communicating the results: The results extracted from the process of Data Analysis reported in the format required by the users to support their decisions.

Data Analysts use the various types of Data visualization techniques like charts, graphs, 3-D presentations, and tables to communicate the results clearly and efficiently to the users.