Data science development is very different from software development, and getting the two to mesh is sometimes like trying to cobble together Tinker Toys with Lincoln Logs. Software development is "Measure twice; cut once," while Data Science is "Cut, cut, cut!" The methodologies and processes that support successful software development do not work for data science projects according to one simple observation: software development knows, with 100% assurance, the expected outcomes, while data science – through data exploration and hypothesis testing, failing and learning – discoversthose outcomes. First introduced in the blog "What's The Difference Between BI Analyst and Data Scientist?", the Data Science Engagement methodology in Figure 1 supports the rapid exploration, rapid testing, and continuous learning Data Science "Scientific Method". Let's review each of these in more detail.