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Getting Started: Building Common Understanding

Technology-based fields like data science, artificial intelligence, and machine-learning are changing quickly—including their definitions. These terms can be confusing and interwoven, and are often used to reference different applications interchangeably. While the Department does not endorse any official definitions, we offer an imperfect framework below as a reference to help educators and education leaders decipher literature and program descriptions in this new space. Further clarifying these terms with yourself and others may help in goal-setting, navigating program selection, and making other design choices with common understanding.

Data Science: an inter-disciplinary field often combining statistics, computer code or manipulation, and domain-specific knowledge. Use-cases can include data analysis, data storage or management, data visualization, and data ethics.
Artificial Intelligence: a very broad term referring to any program, and the development thereof, that enables computers to automate tasks resembling human processes. Use-cases can include visual recognition, image categorization, speech processing, smart voice assistants, and language translation. Many artificial intelligence methods may be considered a subfield of data science, since these methods use large or complex data in the process of achieving automation.
Machine-Learning: a subfield of artificial intelligence that leverages large amounts of data and statistical models to improve an algorithm automatically. Current use-cases can include recommendation algorithms (music, TV, products), autonomous vehicle programs, and most other enterprise AI.
Neural Networks: an increasingly popular class of machine-learning algorithms, which uses many individual decision nodes to learn from data to make predictions. Deep Learning, another increasingly popular algorithmic approach, describes a neural network with many layers.

When building a common understanding of these terms with stakeholders, aiming to teach all these domains and techniques by high school graduation may likely be overwhelming. Instead, PK–12 leaders and educators may choose to focus on identifying more universal data acumen as a foundation for all students on which to build, regardless of their academic or career interests, and consider the ways in which new educational pathways can enable progression to more distinct, specialized fields

What We're Reading

"Realizing the Potential of Data Science," NSF Computer and Information Science and Engineering Advisory Committee, 2016.

"AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What's the Difference?, IBM, 2020.

"Roundtable on Data Science Postsecondary Education," National Academies of Sciences, Engineering, and Medicine, 2020

"Data Literacy Fundamentals," DataCamp. Track with 5 self-guided online courses.