As more and more data is amassed, focus has increased on getting information out of the data. Data Science is becoming mainstream. Machine learning can play a key role when using Data Science to find a solution to a problem. There are numerous algorithms used to programmatically learning about and from data. Random Forest is one that is both popular and powerful. In this talk I present the theory behind Random Forests and the Decision Trees form which they are made. Consider this theory your compass. I then move on to examine the attributes of a small data set. Consider the values and the relationships between them as your map. Finally, with our compass and map in hand I look at a Random Forest implementation that makes predictions from the data without getting lost.