Having introduced our housing finance data, how to work with it at scale, and some key machine learning techniques, we turn to questions of what can go wrong with our analysis, and how to avoid these pitfalls. We tie this to our course theme of inference and specifically causal inference, laying out concepts and techniques in greater detail. We walk through a live example of data wrangling in Python with the pandas library. From there, we'll return to live examples of predicting both discrete and continuous values in both Qlik AutoML and Python. We'll return to the the question: why Python? And finally we'll recap the course, tie some course themes together through a brief discussion of the Alignment Problem, and share resources on how to build on course skills and use them in the housing industry.