What is Data Science?

drawing useful conclusions from large and diverse data sets, through exploration, prediction and inference.
- exploration involves identifying patterns in information
- prediction involves using information to make informed guesses about unknown values
- inference involves quantifying degree of certainty.
primary tools for
- exploration are visualizations and descriptive statistics
- prediction are machine learning and optimization
  - inference statistical tests and models
Statistics is central component of data science because it studies how to make robust conclusions based on incomplete information.
Computing is central component of data science because it allows us to apply analysis techniques to the large and diverse data sets that arise in real-world applications, including
- numbers
  - text
  - images
  - videos
  - 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 inferential and computational tools.

Why Data Science?

The degree of uncertainty for many decisions ca be reduced sharply by
- access to large data sets, and
- computational tools required to analyze them effectively
Data driven decision making has already transformed a tremendous breadth of industries
wide range of academic disciplines are evolving rapidly to incorporate large-scale data analysis into their theory and practice.