The data science process has about 7 steps: 1) data wrangling (getting and cleaning data); 2) exploratory data analysis, statistical inference, and data visualization; 3) feature engineering; 4) development of a predictive model (training, evaluating, optimizing, testing); 5) model deployment; 6) delivery of results (report, story, visualization); 7) model maintenance. For a visual of this process using credit card fraud detection as an example, click here.