1.4 The data analysis process

1. Cleaning the data: investigate the data to make sure that they are applicable to the project goals, accurate, and appropriate
2. Understanding the data: often referred to as exploratory data analysis (EDA). EDA brings to light how the different variables are related to one another, their distributions, typical ranges, and other attributes.
   • “How did I come by these data?”
   • “Is the data relevant?”
3. Develop clear expectations of the goal of your model and how performance will be judged (Chapter 9)
   • “What is/are the performance metrics or realistic goal/s of what can be achieved?”

The data science process (from R for Data Science by Wickham and Grolemund.