# Introduction to Data Science Pipeline

A Data Science pipeline is a process that transforms raw data into useful solutions to solve business issues. 


![Screenshot 2022-10-19 at 2.40.44 PM.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1666170672544/sf8NH0rA9.png align="left")


There are various steps in the data science pipeline, including


- 
### Obtaining information

   This is where data is collected and processed from internal, external, and third-party 
   sources into a useful format (XML, JSON, .csv, etc.).

- 
### Data cleansing

  This is the most time-consuming step. Anomalies in data, such as duplicate
  parameters, missing values, or useless data must be cleaned before a data visualization 
  can be created.

  Data cleansing can be classified into two types:

  a) Examining data to look for errors, missing numbers, or entries that have been 
  corrupted.

  b )Cleaning data entails filling in gaps, correcting errors, deleting duplicates, and 
  discarding obsolete records or data.
 
- 
### Data exploration and modeling

  After the data has been completely cleaned, data visualization tools and charts can be 
  utilized to detect patterns and values. This is where artificial intelligence (AI) techniques 
  come into play. You can detect patterns and apply specific rules to data or models using 
  classification accuracy, confusion matrix, logarithmic loss, etc.

- 
### Data interpretation

 This stage aims to uncover and link insights with your data findings. You can then use 
 charts, dashboards, or reports/presentations to present your results.

I tried to provide all the important information on Data Science pipeline. I hope you will find something useful here. Happy Learning !!