{"id":1027,"date":"2025-12-13T20:38:50","date_gmt":"2025-12-13T20:38:50","guid":{"rendered":"https:\/\/research.ced.ncsu.edu\/esteemhub\/?page_id=1027"},"modified":"2026-06-08T14:19:56","modified_gmt":"2026-06-08T14:19:56","slug":"college-content-courses","status":"publish","type":"page","link":"https:\/\/research.ced.ncsu.edu\/esteemhub\/resources\/college-content-courses\/","title":{"rendered":"College Content Courses"},"content":{"rendered":"\n\n\n\n
\n
\n
\n
\n \n
\n \n\n
\n
\n \"GAISE\n <\/div>\n <\/div>\n \n
\n \n

Guidelines for Assessment and Instruction in Statistics College Report<\/h3>\n \n\n \t \t

Much has changed since the ASA endorsed the Guidelines for Assessment and Instruction in Statistics Education College Report (hereafter called the GAISE College Report) in 2005.\u201d This sentence served as an introduction to the 2016 revised GAISE College Report and served as motivation for undertaking the current revision process. The current revision (2025) will be made available in stages.<\/p>\n\t \t \n

Visit the website \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"CAUSE\n <\/div>\n <\/div>\n \n
\n \n

CAUSEweb<\/h3>\n \n\n \t \t

Arising from a strategic initiative of the American Statistical Association in 2001, CAUSE is a national organization whose mission is to support and advance undergraduate statistics education, in four target areas: resources, professional development, outreach, and research.\u00a0<\/p>\n\t \t \n

Visit the website \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"DISCIS-IS\n <\/div>\n <\/div>\n \n
\n \n

Research Notes from the DISCUS-IS Project<\/strong><\/h3>\n \n\n \t \t

Short, practitioner-oriented posts drawn from ongoing research on inclusive instructional practices in postsecondary introductory statistics. Written for statistics educators, these posts translate findings from student interviews and classroom research into language that is immediately useful in the postsecondary statistics classroom.<\/p>\n\t \t \n

Read more \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"SCORE\n <\/div>\n <\/div>\n \n
\n \n

Score Network<\/h3>\n \n\n \t \t

This national network creates and shares sports datasets and modules specifically designed for teaching statistics and data science.

Content is organized by sports as well as by statistics and data science concepts.<\/p>\n\t \t \n

Visit the website \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"Introduction\n <\/div>\n <\/div>\n \n
\n \n

Introduction to Statistical Investigations<\/h3>\n \n\n \t \t

The text differs from traditional texts in both content and pedagogy.\u00a0 Statistical inference is introduced using simulation-based methods in Chapter 1. Students are actively engaged in the material through numerous activities in which they flip coins, draw cards, use real data, do simulations with free applets and run experiments.\u00a0<\/p>\n\t \t \n

Visit the website \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"META\n <\/div>\n <\/div>\n \n
\n \n

META Math Project<\/h3>\n \n\n \t \t

The META Math Project was designed to create, field-test, and research curriculum materials that connect undergraduate mathematics and statistics courses with high school teaching applications. By integrating high school-level mathematics as a meaningful application area, META aims to deepen mathematical understanding for both prospective teachers and mathematics students. This approach not only enriches connections between undergraduate coursework and secondary education but also fosters more motivated new teachers and more engaged mathematics students.<\/p>\n\t \t \n

Visit the project \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"IMS2\n <\/div>\n <\/div>\n \n
\n \n

Introduction to Modern Statistics (2e)<\/strong><\/h3>\n \n\n \t \t

Introduction to Modern Statistics (2e) is a free, open-access textbook that blends statistical thinking with practical, real-world data analysis. Emphasizing both computational and mathematical approaches, it features multivariable data visualization, simulation-based inference, and regression modeling, all supported by interactive R tutorials and hands-on labs.<\/p>\n\t \t \n

Access textbook \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"Statistical\n <\/div>\n <\/div>\n \n
\n \n

A Simulation Approach to Modeling Uncertainty<\/strong><\/h3>\n \n\n \t \t

This open-access introductory statistics textbook and lab manual are designed to engage students in active, collaborative learning through small-group activities and rich multimedia resources. Centered around the use of TinkerPlots software, the materials emphasize conceptual understanding and data exploration, helping students develop critical habits of mind for reasoning with data.<\/p>\n\t \t \n

Access textbook \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"ADAPT\n <\/div>\n <\/div>\n \n
\n \n

A Project-based Approach to Introductory R and Python for Data Science<\/strong><\/h3>\n \n\n \t \t

This open online book supports learning of R and Python through a data-centric, project-based approach. It guides learners in building programming skills and broader data science thinking by utilizing real-world data science projects. It can be used as a course preview, review, and supplements to other classroom materials.<\/p>\n\t \t \n

Access the text \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n\n\n\n \n\n

\n
\n \"Principles\n <\/div>\n <\/div>\n \n
\n \n

OpenStax Principles of Data Science<\/h3>\n \n\n \t \t

Principles of Data Science is intended to support one- or two-semester courses in data science. It is appropriate for data science majors and minors as well as students concentrating in business, finance, health care, engineering, the sciences, and other fields. The text emphasizes data ethics, AI, and hands-on practice through Python code, examples, and exercises, with downloadable resources included.<\/p>\n\t \t \n

Access open textbook \n \n \n <\/svg><\/span><\/span><\/p>\n <\/div>\n\n <\/a>\n<\/div>\n\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":72,"featured_media":0,"parent":59,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"ncst\/default-header","ncst_dynamicHeaderData":"{}","ncst_content_audit_freq":"","ncst_content_audit_date":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-1027","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/1027","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/comments?post=1027"}],"version-history":[{"count":6,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/1027\/revisions"}],"predecessor-version":[{"id":1496,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/1027\/revisions\/1496"}],"up":[{"embeddable":true,"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/pages\/59"}],"wp:attachment":[{"href":"https:\/\/research.ced.ncsu.edu\/esteemhub\/wp-json\/wp\/v2\/media?parent=1027"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}