The data science process has about 7 steps: 1) data wrangling (getting and cleaning data); 2) exploratory data anlysis, statistical inference and data visualization; 3) feature engineering; 4) model development (training, evaluating, optimizing, testing); 5) model deployment; 6) delivery of results (report, story, visualization); 7) model maintenance. You may work on this whole process or a piece of it, but should understand what happens at each step.