(Later in our course we will review simple linear regression and hypothesis testing.) If you have taken other relevant courses in statistics, mathematical modeling, econometrics, etc., and want to bring that knowledge in to use in this course, great, but it’s not a requirement. You’ve learned some introductory statistical analysis in one of the course prerequisites (GB213), and we will leverage that. Our course focuses on all the steps except for the analysis. Report those conclusions to the relevant stakeholders. (The analysis is the step most people think of as data science, but it’s just one step! Notice how much more there is that surrounds it.) Perform analysis, model building, testing, etc. Obtain data that you hope will help answer the question. Here’s a typical workflow for how that plays out in practice. Turning data into actionable value usually involves answering questions using data. The professional society INFORMS defines the related field of analytics as “the scientific process of transforming data into insight for making better decisions.” Regardless of whether data science is just a part of statistics, and regardless of the domain to which we’re applying data science, the goal is the same: to turn data into actionable value. This echoes a famous blog post by Drew Conway in 2013, called The Data Science Venn Diagram, in which he drew the following diagram to indicate the various fields that come together to form what we call “data science.” From application domains in business and the sciences comes challenges worthy of battle, and evaluation standards to assess when they have been adequately conquered. From statistics comes a long tradition of exploratory data analysis, significance testing, and visualization. From computer science comes machine learning and high-performance computing technologies for dealing with scale. I think of data science as lying at the intersection of computer science, statistics, and substantive application domains.
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