Role of Data Science in Statistical and Machine Learning Approaches

Vocabulary terms and definitions highlighting the roles, foundational disciplines, and key concepts of data science, statistics, and machine learning based on the lecture.

Data Science

An interdisciplinary field coined by statisticians in the '90s that acts as a connective tissue between raw data and actionable intelligence by combining mathematics, statistics, computer science, and domain expertise.

Analytical Engine

The foundational role played by statistics and mathematics in data science, utilizing linear algebra, calculus, and probability theory to understand probabilistic data distributions, test hypotheses, and quantify uncertainty.

Tools for Scale

The foundational discipline provided by computer science and information technology, including data engineering, algorithm design, and high-performance computing needed to process and store big data.

Domain Expertise

The foundational pillar involving deep understanding of a specific industry (e.g., finance, healthcare, meteorology, hydroclimatology) necessary to pose the right questions and interpret analytical results meaningfully.

Statistics Focus and Goal

Focuses on mathematical modeling and inference, with the specific goal of quantifying uncertainty and proving relationships.

Machine Learning Focus and Goal

Focuses on algorithms that learn from data, with the goal of building generalized predictive models that improve with experience.

Data Science Focus and Goal

Focuses on the end-to-end life cycle of data, with the goal of extracting meaningful knowledge, insights, and predictability.

Big Data

A term used when a data set is exceptionally large, necessitating high-performance computing and information technology tools for handling and processing.

Simulated Data

Data produced by models (such as climate models) that preserves statistical properties, useful for applications in engineering, finance, and earth sciences where observed data is unavailable.