The Data Science process is a structured workflow that data scientists follow to extract insights, solve problems, and make informed decisions using data. While different methodologies may vary slightly, the general process involves several key stages that help transform raw data into actionable insights. The typical steps in the data science process are as follows: 1. Problem Definition Goal : Understand and clearly define the problem or question that the data science project aims to solve. Key Questions : What is the business or research objective? What are the expected outcomes? What are the success criteria? Example : For an e-commerce platform, the problem could be to predict customer churn or recommend products to increase sales. 2. Data Collection Goal : Gather all relevant data from various sources that can help in addressing the problem. Sources : This can include internal databases, APIs, web scraping, third-party data, or surveys. Challenges : The data might be in diff...
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