Data science is a discipline that makes understandings from data. Data science is used in fields as diverse as biology, economics, finance, statistics, and of course computer science, to name a few. As such data science is an interdisciplinary field and has been in turn influenced by these fields in many ways as well.

To illustrate the interdisciplinary nature of data science, here are some examples of how data science is used in other fields:

• Biology: The data set we will use in this course was collected by a biologist; the field of bioinformatics lies at the intersection of biology and data science which uses data science to interpret biological data, identify genetic variations, analyze gene expression, and predict protein structures.
• Economics: Common economic measurements, including Gross Domestic Product (GDP), inflation, and unemployment, heavily depend on extensive datasets and frequently employ similar forecasting techniques utilized in the field of data science.
• Finance: Stock market investment decisions are data driven and data science can be used to forecast stock prices, prepare reports, find trends, make predictions, and recently to automate trading.
• Health Care: Patient data can by analyzed with data science techniques to improve patient care, identify high risk patients, determine the optimal drug as a treatment effect, and to predict disease outbreaks.
• Retail: Customer data as well, may be analyzed to improve the customer experience, understand consumer behavior, predict customer churn, optimize pricing, and improve marketing and sales strategies.

It is also worth noting that much of the theory that data science is built upon has origins in Statistics, and also that data science can only exist thanks to advances in Computer Science in being able to create computers that can perform the advanced computations that are required in many data science applications.

Thanks to data science's interdisciplinary nature, it is very welcoming to people whom may have studied a different discipline before making the transition.