Data story: Statistician vs AI/ML Engineer vs Data Analyst?

I used to think the three roles were pretty much the same. However, today I finally understood the differences between them while working through the Google Data Analytics course on Coursera. I’ve been at it for the past six months, and I’m amazed Coursera still lets me continue! 😀

> Statisticians: They are careful that decision makers do not come to a wrong conclusion. 

> Machine learning and AI engineer: I can build an automation system that can perform task with 99.99% accuracy

> Data analyst: The excellence of an analyst is SPEED! Can you surf through vast amount of data to explore it and discover the potential in the data worth knowing about and bringing to your decision makers! Data analysis process is as below:

>>Ask: business challenge, objective, or question

>>Prepare: data generation, collection, storage, and data management

>>Process: data cleaning and data integrity

>>Analyze: data exploration, visualization, and analysis

>>Share: communicating and interpreting results 

>>Act:  putting insights to work to solve the problem