Difference between Data Analyst, Data Scientist, and Machine Learning Engineer in less than 30 seconds.

A Data Analyst looks at the data to see what happened and tells you what to do in the future. They typically use Excel, SQL, and reporting tools such as Tableau. A very simple example of a Data Analyst project is reporting sales for the last six months or one year. A Data Scientist takes it one step further where they look at the data, use statistics, and pre-built machine learning models to tell you what's going to happen in the future. Now, prediction is a subset of tasks that a Data Scientist would do, but I'm using this as an example to explain the difference. The typical Data Scientist toolkit includes SQL, Python, statistics, and pre-built machine learning models. A Machine Learning Engineer takes it even one step further where they use existing pre-built machine learning models but also customize and build new models to apply to the data to solve business problems and come up with solutions. The primary toolkit of a Machine Learning Engineer is Python, machine learning concepts, math, statistics, and sometimes SQL. But you already knew all of this because you follow me.