Data has been statisticians and analysts’ friend for hundreds of years. Tabulated data are the most common format that we use daily. People used to store data on papers, tapes, diskettes, or hard drives. Only recently, with the development of computer hardware and software, the volume, variety, and speed of the data exceed the capacity of a traditional statistician or analyst. So using data becomes a science that focuses on the question: how can we store, access, process, analyze the massive amount of data and provide actionable insights? In the past few years, by utilizing commodity hardware and open-source software, people created a big data ecosystem for data storage, data retrieval, and parallel computation. Hadoop and Spark have become a popular platform that enables data scientists, statisticians, and analysts to access the data and to build models. Programming skills in the big data platform have been an obstacle for a traditional statistician or analyst to become a successful data scientist. However, cloud computing reduces the difficulty significantly. The user interface of the data platform is much more friendly today, and people push much of the technical details to the background. Today’s cloud systems also enable quick implementation of the production environment. Now data science emphasizes more on the data itself, models and algorithms on top of the data, rather than the platform, infrastructure and low-level programming such as Java.