Chapter 12: A Collection of Best Practices
This chapter serves as a special chapter to investigate three important topics that are prevalent in data science nowadays: data source quality, data visualization quality, and causality interpretation. This has generally been a missing chapter in peer publications, but I consider it essential to stress the following topics. I want to affirm that you, as a future data scientist, will practice data science while following the best practice tips as introduced in this chapter.
After finishing this chapter, you will be able to do the following:
- Understand the importance of data quality
- Avoid using misleading data visualization
- Spot common errors in causality arguments
First, let's start with the beginning of any data science project: the data itself.
