The Data Science Process
- [The Data Science Process - ResearchGate]
- Overview
Data science is the process of using collected data to collaborate and solve real-world problems using computer scientists, statisticians, and subject matter experts. It combines mathematics, statistics and programming to find hidden patterns and derive answers. But to discover its full potential, we need to follow specific steps called the data science process.
The data science process is a dynamic and iterative process that transforms raw data into valuable insights.It combines the art of asking the right questions, the science of extracting knowledge from data, and the skill of communicating meaningful findings.It involves various stages, including problem definition, data collection, preprocessing, exploratory analysis, model building and deployment.
Throughout the process, data scientists employ a range of techniques, algorithms and tools to unlock the potential hidden in data and drive data-based decisions. By adopting data science processes, individuals and organizations can harness the power of data to gain a competitive advantage, uncover new opportunities, and make impactful discoveries.
The simple linear form of data science process consists of following five distinct activities (stages) that depend on each other:
- Stage 1: Acquire - To Obtain Data
- Stage 2: Prepare - To Scrub Data
- Stage 3: Analyze - To Explore Data
- Stage 4: Report - To Model Data
- Stage 5: Act - To Interpret Models and Data
[More to come ...]
