# What Is Data Science? Process, Applications, and Key Differences Explained

What is data science?

[Data science is the stu](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=hashnode)dy of data [to extract m](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat)eaningful insights for business. It is an approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze and to drive business decision-making. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

## How Data Science Works

The proces[s typically](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat) follows thes[e stages:](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat)

1. Data Collection/Ingestion: Gatheri[ng raw data](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat) from sources like databases, IoT, logs, APIs.
    
2. Data Cleaning & Preparation: Handl[ing missing](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat) values, duplicates, transformation via ETL processes.
    
3. Exploratory Data Analysis (EDA): I[dentifying p](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat)atterns, biases, ranges in data.
    
4. Modeling & Analytics: Building mod[els for desc](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat)riptive, diagnostic, predictive, and prescriptive analytics.
    
5. Communication: Visualizing and rep[orting insig](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat)hts for stakeholders using tools like Python, R, BI platforms.
    
6. Deployment & Automation: Operation[alizing mode](https://dataplatr.com/blog/data-science?utm_source=Article_Submission+&utm_medium=muamat)ls and enabling continuous improvement.