What is Data Science?
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Data Science is about drawing useful conclusions from large and diverse data
sets through exploration, prediction, and inference.  Exploration involves
identifying patterns in information.  Prediction involves using information
we know to make informed guesses about values we wish we knew.  Inference
involves quantifying our degree of certainty: will the patterns that we found in our data also appear in new observations? How accurate are our predictions? Our primary
tools for exploration are visualizations and descriptive statistics, for
prediction are machine learning and optimization, and for inference are
statistical tests and models.

Statistics is a central component of data science because statistics
studies how to make robust conclusions based on incomplete information. Computing
is a central component because programming allows us to apply analysis
techniques to the large and diverse data sets that arise in real-world
applications: not just numbers, but text, images, videos, and sensor readings.
Data science is all of these things, but it is more than the sum of its parts
because of the applications. Through understanding a particular domain, data
scientists learn to ask appropriate questions about their data and correctly
interpret the answers provided by our inferential and computational tools.
