Software Engineering Principles for Data Science introduces the methods, practices and tools that make data science work reliable and maintainable: the software development life cycle and CRISP-DM, version control and team collaboration with Git, writing clean, tested and documented Python, basic development practices such as planning and code review, and designing simple user interfaces. You learn by doing, through hands-on labs, forums and a practical case study on your own computer.