What is data analysis? 

Data analysis infers to a process of cleaning, inspecting, and modeling data and to discover valuable information, bringing to a conclusion and that which supports decision making. Analyzing data entails numerous facets and approaches necessitating varied techniques under various names and domains.

What is the data analysis technique? 

Numerous types of analysis techniques exist based on technology and business; significant data analysis techniques include:

  • Prescriptive Analysis
  • Text Analysis
  • Statistical Analysis
  • Predictive Analysis
  • Diagnostic Analysis

Prescriptive Analysis

This analysis combines the insight from the previous study in determining which action to take in the immediate decision or problem.

Text Analysis

Also identified as data mining, this method helps in discovering patterns in large sets of data using databases or data mining tools. This used to transform the raw data into information. In a general view, text analysis offers a way to extract and examine data alongside deriving patterns and finally interpreting the data.

Statistical Analysis

The statistical analysis illustrates the happenings by using past data in the form of a dashboard. The statistical analysis includes data analysis, collection, presentation, interpretation, and modelling. (Descriptive analysis and inferential analysis)

Predictive Analysis

The following analysis predicts future outcomes founded on the present or past data. The accuracy in the subsequent investigation is based on how much detailed information one has and how much the researcher digs in it.

Diagnostic Analysis

This illustrates why it happened by identifying the cause of the insight found in statistical analysis. This analysis is essential as it determines the behavior pattern of data. 

What is the Data analysis process?

In summary, below are the necessary steps to analyze data and solve problems

  1. Define Analytic Objective – As said, defining your question is 50% of the solution, so you need to set your problem and the scope of your analysis.
  2. Extract Input data – Based on the problem description, you will need to select your input data and extract them for analysis carefully. 
  3. Validate input data – Check the input data for accuracy and consistency.
  4. Repair input data – Fix what could be there in data like null values.
  5. Transform input data – Apply the required transformations for each field if necessary.
  6. Apply analysis – Perform your analysis using your preferred tool and algorithm.
  7. Generate deployment method – Build the deployment package for your model.
  8. Assess results – Check and validate your conclusion to make sure they are accurate.
  9. Refine analytic objective – Refine your algorithm or analysis method if required
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