Eu no . estou l com vrias contas <| sep |> Real talk though why did you let the money walk <| sep |> Duskwalker ' s and Reins of the Astral Cloud Serpent does it for me edit words <| sep |> Here is a helpful diagram 0 cbXveb 1 J 0 VFtjJZ 4 rqIAAAAAAAAMHMbS 32 Pio 2 a 1 IFOyp 5 T 86 S 0 jiyB 3 KAN 1 iwCEws 1600 download % 2 B % 25281 % 2529 . pngEssentially The best place to begin is with a Math Stats background . Then learn a programming language . Any one of your choice , but most people in the industry use Python and Java . A lot of forums talk about R , but this doesn ' t get used much outside of academia . Adding in the computer science aspect is key , since you ' ll need to move around and manipulate large sets of data . How should you manipulate the data ? Well your stats background will tell you how to get a useful answer out of it . Finally , you ' ll need some substantial expertise in some field or industry . Healthcare , finance , etc . This is the science part of Data Science , so you ' ll want a master level skillset in the science of your choice . Being able to search out desired results and communicate those results are the key function of any scientist , and so are essential to good Data Science . A lot of managers can be dazzled by pretty charts and diagrams , but at the end of the day it ' s the results that drive the bottom line . Now these are the base things for being a data scientist . Getting $ 113 , 000 a year to do it is another story . The industry is flooded with a lot of data scientists who should really be data engineers , or computer scientists , or statisticians . Mainly because their skillset and job descriptions fall more into those categories than they do into a data scientist role . But the Data Science is young and hip , and Big Data is a buzzword for companies . Managers want to get on the bandwagon without really knowing what Big Data is or what Data Science is .