Guidelines for Undergraduate Programs in Statistics Beth Chance – Cal Poly Feedback can be sent to Rebecca Nichols, ASA Director of Education

Background/Motivation More statistical content in lower grade levels Statistics enrollments are increasing Bachelor’s degrees (673 in 2003; 1656 in 2013) Increased need for graduates who can “think with data” McKinsey report: Shortage of 140,000 to 190,000 million people with deep analytic skills… 1.5 million managers (who can use data) to make effective decisions Prior guidelines approved by ASA Board in /8/2016Joint Mathematics Meetings2 NCES Digest of Education Statistics

Background Spring 2013 incoming ASA President Nat Schenker appointed a working group with representatives from academia, industry, and government to make recommendations Beth Chance (Cal Poly), Steve Cohen (NSF), Scott Grimshaw (BYU), Johanna Hardin (Pomona), Tim Hesterberg (Google), Roger Hoerl (Union), Nicholas Horton (Amherst, Chair), Chris Malone (Winona State), Rebecca Nichols (ASA), and Deborah Nolan (Berkeley) New guidelines were endorsed by the Board of Directors of the American Statistical Association on November 15, /8/2016Joint Mathematics Meetings3

The American Statistical Association endorses the value of undergraduate programs in statistics as a reflection of the increasing importance of the discipline. We expect statistics programs to provide sufficient background in the following core skill areas: statistical methods and theory, data management, computation, mathematical foundations, and statistical practice. Statistics programs should be flexible enough to prepare bachelor's graduates to either be functioning statisticians or go on to graduate school. 1/8/2016Joint Mathematics Meetings4

ASA Guidelines Principles Skills needed Curriculum topics (Bachelor’s Degree) Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings5

ASA Guidelines Principles Equip students with quantitative skills to use in flexible ways Emphasize concepts and tools for working with data Provide experience with design and analysis Distinct from mathematics Skills needed Curriculum topics (Bachelor’s Degree) Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings6

ASA Guidelines Principles Skills needed Curriculum topics (Bachelor’s Degree) Curriculum topics (Minors/Concentrations) Additional resources o Statistical o Mathematical o Computational o “Statistical practice” o Substantive Area 1/8/2016Joint Mathematics Meetings7

ASA Guidelines Principles Skills needed Curriculum topics (Bachelor’s Degree) Content Pedagogy Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings8

ASA Guidelines Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources o Statistical topics Statistical theory Exploratory and graphical data analysis methods Statistical modeling (parametric and non- parametric) Design of studies and issues of bias, causality, and confounding 1/8/2016Joint Mathematics Meetings9

ASA Guidelines o Statistical topics o Mathematical topics, probability Calculus (integration and differentiation) through multivariable calculus Applied linear algebra Probability Emphasis on connections between these concepts and their applications in statistics Why and How statistical methods work Communicate in language of mathematics Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings10

ASA Guidelines o Statistical topics o Mathematical topics, probability o Computational topics Algorithmic thinking/problem solving Programming concepts and Higher-level languages Ability to access data in variety of ways Database concepts and technology Computationally intensive methods Reproducibility Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings11

ASA Guidelines o Statistical topics o Mathematical topics, probability o Computational topics o “Statistical practice” topics Effective technical writing, presentations, and visualizations with technical and nontechnical audiences Ethical standards of practice Teamwork and collaboration Planning for data collection Data management The undergraduate experience should include an internship, "capstone" course, consulting experience, or a combination Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings12

ASA Guidelines o Statistical topics o Mathematical topics, probability o Computational topics o “Statistical practice” topics o Pedagogy Emphasize real data and authentic applications Present data in a context that is both meaningful to students and indicative of the science behind the data Include experience with statistical computing Encourage synthesis of theory, methods, and applications Offer frequent opportunities to develop communication skills Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources 1/8/2016Joint Mathematics Meetings13

Key Skills 1/8/2016Joint Mathematics Meetings14 Effective statisticians at any level display an integrated combination of skills (statistical theory, application, data and computation, mathematics, and communication) Students need scaffolded exposure to develop connections between statistical concepts/theory and their application to statistical practice Need multiple opportunities to analyze messy data using modern statistical practices Programs should provide their students with sufficient background in each of these areas

Key Changes Increased importance of data-related skills Embrace a more comprehensive view of modeling Model building, explanatory models, predictive modeling, etc. Continue to enhance experiences that promote unstructured learning and enhance teamwork Research, Capstones, Internships, REU, etc. (non textbook data) Continue to promote communication skills Multiple opportunities to practice 1/8/2016Joint Mathematics Meetings15

Key Points “These guidelines are intended to be flexible while ensuring that programs provide students with the appropriate background along with necessary critical thinking and problem-solving skills to thrive in our increasingly data-centric world. Programs are encouraged to be creative with their curriculum to provide a synthesis of theory, methods, computation, and applications.” 1/8/2016Joint Mathematics Meetings16

ASA Guidelines General statistical methodology Statistical modeling (e.g., multiple regression, confounding, diagnostics) Facility with professional statistical software, data management skills plus elective topics, capstone and/or relevant courses from other departments 1/8/2016Joint Mathematics Meetings17 Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources

ASA Guidelines Resource Material for Minors/Concentrations White papers (Ethics, Internships, Smaller Programs, Learning Outcomes) November 2015 issue of The American Statistician Webinar series 1/8/2016Joint Mathematics Meetings18 Principles Skills needed Curriculum topics (Bachelor’s Degrees) Curriculum topics (Minors/Concentrations) Additional resources

Next Steps Faculty development Engagement with two year colleges Surveys of graduates and employers Certification/accreditation pathway Multiple pathways for introduction to statistics Periodic review 1/8/2016Joint Mathematics Meetings19

Example – Cal Poly curriculum proposals (2015) Orientation to the major History, Big Data, Ethics, Communication, Computing (R) Introductory sequence Simulation-based, integration of theory and applications, R Markdown Sophomore level communication course In addition to senior level communication and consulting course Regression modelling Predictive modelling (e.g., splines, classification trees) Linear models course More electives Survival analysis, Applied probability models, Multi-level data Launch of Interdisciplinary Minor in Data Science (with Computer Science) 1/8/2016Joint Mathematics Meetings20

Parting Questions What is the dividing line between a bachelor’s degree and a master’s degree (2013)? What types of careers can BS students find? Can we improve student preparation for life after graduation? Creative solutions? Capstones Substantive area Big data How develop “statistical practice” skills 1/8/2016Joint Mathematics Meetings21

Recommendations Master’s Degree Programs Graduates should have a solid foundation in statistical theory and methods. Programming skills are critical and should be infused throughout the graduate student experience. Communication skills are critical and should be developed and practiced throughout graduate programs. Collaboration, teamwork, and leadership development should be part of graduate education. Students should encounter non-routine, real problems throughout their graduate education. Internships, co-ops, or other significant immersive work experiences should be integrated into graduate education. Programs should be encouraged to periodically survey recent graduates and employers of their recent graduates as a means of evaluating the success of their programs and to examine if other programmatic changes are warranted. 1/8/2016Joint Mathematics Meetings22