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Curriculum Guidelines for Undergraduate Programs in Statistical Science


The ASA is updating the undergraduate curriculum guidelines. Feedback can be sent to the working group chair, Nicholas Horton, at nhorton@amherst.edu.


Guidelines for Undergraduate Statistics Programs Webinar Series

The American Statistical Association endorses the value of undergraduate programs in statistical science, both for statistical science majors and for students in other majors seeking a minor or concentration. Guidelines for such programs were endorsed in 2000, and a new workgroup is working to update them.

To help gather input and identify issues and areas for discussion, the workgroup has organized a series of webinars to focus on different issues. These are free to attend, and will be made available after the event.

We hope that you can participate in some or all of the following webinars. These will be recorded and made available for review after the session at the guidelines webpage. To sign up to participate in a particular live webinar, please email Rebecca Nichols (rebecca@amstat.org).

Windows Media Video Observations from Large Programs (PowerPoint Slides)
Friday, September 27th, 12:00-12:45pm Eastern Time

Description: Different institutions implement statistics programs in different ways. In this webinar, we will hear from representatives from four large undergraduate statistics programs (Berkeley, BYU, Harvard and NCSU) and discuss the existing guidelines and issues which should be addressed in the future.

Windows Media Video Connection with Community Colleges (PowerPoint Slides)
Monday, October 21st, 6:00-6:45pm Eastern Time

Description: Community colleges serve a key role in the US higher education system, accounting for approximately 40% of all enrollments. In this webinar, representatives from community colleges and universities with many community college transfers will discuss the interface between the systems and ways to prepare students for undergraduate degrees and minors in statistics.

Windows Media Video Building Towards Big Data and Data Science (PowerPoint Slides)
Monday, November 18th, 6:00-6:45pm Eastern Time

Description: Undergraduate Statistics majors and minors will be entering an increasingly data-centric world upon graduation. What new skills and capacities will they need to succeed in this environment? How do we train the current generation of faculty to be able to teach them? In this webinar, data scientists and faculty will work to enunciate key aspects which need to be included in our programs.

Windows Media Video The Role and Variety of Undergraduate Statistics Capstones (PowerPoint Slides)
Wednesday, December 4th, 5:00-5:45pm Eastern Time

The undergraduate capstone experience brings reflection and focus as well as integration of material studied within a major. Capstones require the disciplined use of skills, methodology and knowledge taught throughout their courses, experiential and co-curricular activities. In this webinar, several models for undergraduate capstones will be discussed, as well as key themes which pervade them all.

Windows Media Video Preparing Statistics Majors for Graduate Study (Perhaps Your Own!) (PowerPoint Slides)
Friday, February 7th, 2:00-2:45pm Eastern Time

The current guidelines were designed to prepare students for graduate school, and the webinar's participants will share their program's experience with statistics majors. In addition to reflecting on the strengths and weaknesses of current undergraduate preparation for graduate study, the webinar will look to future expectations (particularly with regard to big data and computing) for admission in statistics, biostatistics, and analytics programs.


To be added to a low-volume, moderated mailing list for announcements related to the revision of the undergraduate guidelines, please contact Rebecca Nichols (rebecca@amstat.org). More information about the workgroup and the process can be sent to the chair (Nicholas Horton, nhorton@amherst.edu).

The American Statistical Association endorses the value of undergraduate programs in statistical science, both for statistical science majors and for students in other majors seeking a minor or concentration. This document provides guidelines for development of curricula for such programs.

Principles

Undergraduate programs in statistics are intended to equip students with quantitative skills that they can employ and build on in flexible ways. Some students will plan graduate work in statistics or other fields, while others will seek employment after their first degree. Programs should be sufficiently flexible to accommodate varying goals. Undergraduate programs are not intended to train professional statisticians, though some graduates may reach this level through work experience and/or further study.

Institutions vary greatly in the type and intensity of programs they are able to offer. The ASA believes almost all institutions can provide a level of statistical education that is useful to both students and employers. We encourage flexibility in adapting these guidelines to institutional constraints. In many cases, statistics minors or concentrations for quantitatively oriented students in fields such as biology, business, and behavioral and social science may be more feasible than a full statistics major.

Undergraduate statistics programs should emphasize concepts and tools for working with data and provide experience in designing data collection and analyzing real data that go beyond the content of a first course in statistical methods. The detailed statistical content may vary, and may be accompanied by varying levels of study in computing, mathematics, and a field of application.

Though statistics requires mathematics for the development of its underlying theory, statistics is distinct from mathematics and uses many nonmathematical skills; thus, the curriculum must be more than a sequence of mathematics courses. It is essential that faculty trained in statistics and experienced in working with data be involved in developing statistics programs and teaching or supervising courses required by the programs.


Skills Needed

Effective statisticians at any level display a combination of skills that are not exclusively mathematical. Programs should provide some background in the following areas:

Statistical - Graduates should have training and experience in statistical reasoning, in designing studies (including practical aspects), in exploratory analysis of data by graphical and other means, and in a variety of formal inference procedures.

Mathematical - Undergraduate major programs should include study of probability and statistical theory, along with the prerequisite mathematics, especially calculus and linear algebra. Programs for nonmajors may require less study of mathematics. Programs preparing for graduate work may require additional mathematics.

Computational - Working with data requires more than basic computing skills. Programs should require familiarity with a standard statistical software package and encourage study of data management and algorithmic problemsolving.

Nonmathematical - Graduates should be expected to write clearly, speak fluently, and have developed skills in collaboration and teamwork and organizing and managing projects. Academic programs often fail to offer adequate preparation in these areas.

Substantive area - Because statistics is a methodological discipline, statistics programs should include some depth in an area of application.


Curriculum Topics for Undergraduate Degrees in Statistical Science

The approach to teaching the following topics should:

  • 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

Statistical Topics

  • Statistical theory (e.g., distributions of random variables, point and interval estimation, hypothesis testing, Bayesian methods)
  • Graphical data analysis methods
  • Statistical modeling (e.g., simple, multiple, and logistic regression; categorical data; diagnostics; data mining)
  • Design of studies (e.g., random assignment, replication, blocking, analysis of variance, fixed and random effects, diagnostics in experiments; random sampling, stratification in sample surveys; data exploration in observational studies)

Mathematical Topics

  • Calculus (integration and differentiation) through multivariable calculus
  • Applied linear algebra (emphasis on matrix manipulations, linear transformations, projections in Euclidean space, eigenvalue/eigenvector decomposition and singular-value decomposition)

Probability

  • Emphasis on connections between concepts and their applications in statistics

Computational Topics

  • Programming concepts; database concepts and technology
  • Professional statistical software appropriate for a variety of tasks

Nonmathematical Topics

  • Effective technical writing and presentations
  • Teamwork and collaboration
  • Planning for data collection
  • Data management

Electives - There are many electives that might be included in a statistics major. As resources will vary among institutions, the identification of what will be offered is left to the discretion of individual units.

Practice - When possible, the undergraduate experience should include an internship, senior-level "capstone" course, consulting experience, or a combination of these. These and other opportunities to practice statistics should be included in a variety of venues in an undergraduate program.


Curriculum Topics for Minors or Concentrations in Statistical Science


The core of a minor or concentration in statistics should consist of the following:

  • General statistical methodology (statistical thinking, descriptive, estimation, testing, etc.)
  • Statistical modeling (simple and multiple regression, diagnostics, etc.)
  • Exposure to professional statistical software

The number of credit hours for minors or concentrations will depend on the policies set by the academic units involved. Additional topics to complete the required number of credit hours could be chosen from some nonexhaustive list (e.g., mathematical statistics, design of experiments, categorical data analysis, time series, Bayesian methods, probability, database management, a capstone experience). Courses from other departments with significant statistical content might be allowed to count toward a statistics minor or concentration, though the content of such courses must differ substantially from the others.

Resources for Statistics Undergraduate Minors/Concentrations

Position Papers