Common mistakes in data science projects include: 1) Not properly defining the business problem or focusing on optimizing the wrong process. 2) Not adequately preparing the data or understanding how it was generated. 3) Rushing the modeling process or implementation without proper testing. 4) Choosing complex methods or "AI" solutions when simpler approaches may work better. 5) Not involving experienced people or adequately educating the team. To avoid these mistakes, it is important to carefully analyze the business problem, data, modeling process, and make sure the right people are involved.