This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and Data Science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—Machine Learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. I’ll posit that learning and practicing data science is hard. It is hard because you are expected to be a great programmer who not only knows the intricacies of data structures and their computational complexity but is also well versed in Python and SQL. Statistics and the latest Machine Learning predictive techniques ought to be a second language to you, and naturally you need to be able to apply all of these to solve actual business problems that may arise. But the job is also hard because you have to be a great communicator who tells compelling stories to nontechnical stakeholders who may not be used to making decisions in a data-driven way.