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Guidelines for Undergraduate Programs in Statistics Beth Chance – Cal Poly ([email protected]) Feedback can be sent to Rebecca Nichols, ASA Director of Education ([email protected]) 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 2000 NCES Digest of Education Statistics 1/8/2016 Joint Mathematics Meetings 2 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, 2014. 1/8/2016 Joint Mathematics Meetings 3 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. 1/8/2016 Joint Mathematics Meetings 4 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degree) • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 Joint Mathematics Meetings 5 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • 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 1/8/2016 Joint Mathematics Meetings 6 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degree) • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 Joint Mathematics Meetings o Statistical o Mathematical o Computational o “Statistical practice” o Substantive Area 7 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degree) • Content • Pedagogy • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 Joint Mathematics Meetings 8 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles o Statistical topics • Statistical theory • Skills needed • Exploratory and graphical data analysis methods • Curriculum topics • Statistical modeling (parametric and non(Bachelor’s Degrees) parametric) • Design of studies and issues of bias, causality, and • Curriculum topics confounding (Minors/Concentrations) • Additional resources 1/8/2016 Joint Mathematics Meetings 9 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles o Statistical topics • Skills needed o Mathematical topics, probability • Calculus (integration and differentiation) • Curriculum topics through multivariable calculus (Bachelor’s Degrees) • Applied linear algebra • Curriculum topics • Probability (Minors/Concentrations) • Emphasis on connections between these concepts and their applications in statistics • Additional resources • Why and How statistical methods work • Communicate in language of mathematics 1/8/2016 Joint Mathematics Meetings 10 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm o Statistical topics • Principles o Mathematical topics, probability • Skills needed o Computational topics • Curriculum topics • Algorithmic thinking/problem solving (Bachelor’s Degrees) • Programming concepts and Higher-level • Curriculum topics languages (Minors/Concentrations) • Ability to access data in variety of ways • Additional resources • Database concepts and technology • Computationally intensive methods • Reproducibility 1/8/2016 Joint Mathematics Meetings 11 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degrees) • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 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 Joint Mathematics Meetings 12 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degrees) • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 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 Joint Mathematics Meetings 13 Key Skills • 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 1/8/2016 Joint Mathematics Meetings 14 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 1/8/2016 Joint Mathematics Meetings 15 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.” 1/8/2016 Joint Mathematics Meetings 16 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degrees) • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 • 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 Joint Mathematics Meetings 17 ASA Guidelines http://www.amstat.org/education/curriculumguidelines.cfm • Principles • Skills needed • Curriculum topics (Bachelor’s Degrees) • Curriculum topics (Minors/Concentrations) • Additional resources 1/8/2016 • Resource Material for Minors/Concentrations • White papers (Ethics, Internships, Smaller Programs, Learning Outcomes) • November 2015 issue of The American Statistician • Webinar series Joint Mathematics Meetings 18 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 1/8/2016 Joint Mathematics Meetings 19 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) 1/8/2016 Joint Mathematics Meetings 20 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? • • • • 1/8/2016 Capstones Substantive area Big data How develop “statistical practice” skills Joint Mathematics Meetings 21 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. 1/8/2016 Joint Mathematics Meetings 22