Having understood what data science is and the nature of data itself, we now look at how data science projects typically proceed. Like software development, data science follows a general workflow, a sequence of steps often iterated upon to move from an initial question to a final insight or product.

This chapter introduces the common stages of the data science process. You will learn about:

• The overall structure of a typical data science workflow.
• The significance of clearly defining the problem you aim to solve.
• Different methods for obtaining data (data acquisition).
• An overview of data preparation necessities.
• The purpose of Exploratory Data Analysis (EDA).
• A basic introduction to modeling concepts.
• The importance of communicating the outcomes of your analysis.

Understanding this process provides a framework for approaching data-related tasks systematically.