Data Literacy / Data Science

Last Updated 03/30/2024

Data literacy and data science skills are important for jobs in fields in business, engineering mathematics, statistics, computer science, life sciences, social sciences, digital humanities, and others. They are also important skills for navigating an increasingly data-driven world. These terms are useful especially to education and training programs established to develop learners’ skills, K-12 through postsecondary education.

Data literacy refers to foundational knowledge about and the ability to read, understand, and communicate data or claims derived from data. Literacy includes knowing how to communicate and question data and representations of data critically, including limitations and potential biases. Areas of knowledge include understanding probability and randomness, ways to visualize data, the meaning of descriptive statistics, the concept of statistical significance, the concept of mathematical techniques called hypothesis tests, how data is collected, and the concept of control groups.

Data science is an interdisciplinary field that refers to applying the processes of working with data. Applications can include calculating means and medians, formatting and graphing data including with multiple variables, performing a scientific study which includes establishing framing questions and crafting the methods of data collection, and measuring variation among the data collected.

Request an Edit

Have something to add or refine? Your input in this work matters greatly and we look forward to reviewing your additions

Organizations (274)

Initiatives (296)

Topics (93)