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Curriculum Guidelines for Undergraduate Programs in Statistical Science


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 manipulation, 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.

The widely cited McKinsey report states that "by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions". A large number of those will be at the bachelor's level. The number of bachelor's graduates in statistics has increased by more than 140% since 2003 (21% from 2012 to 2013).

Much has changed since the previous guidelines were disseminated in 2000. The 2014 guidelines reflect changes in curriculum and suggested pedagogy. Institutions need to ensure that students entering the workforce or heading to graduate school have the appropriate capacity to "think with data" and to pose and answer statistical questions.

Key points include:

Increased importance of data science: Working with data requires extensive computing skills. To be prepared for statistics and data science careers, students need facility with professional statistical analysis soft- ware, the ability to access and manipulate data in various ways, and the ability to perform algorithmic problem-solving. In addition to more traditional mathematical and statistical skills, students should be fluent in higher-level programming languages and facile with database systems.

Real applications: Data should be a major component of statistics courses. Programs should emphasize concepts and approaches for working with complex data and provide experiences in designing studies and analyzing non-textbook data.

More diverse models and approaches: Students require exposure to and practice with a variety of predictive and explanatory models in addition to methods for model building and assessment. They must be able to understand issues of design, confounding, and bias. They need to know how to apply their knowledge of theoretical foundations to the sound analysis of data.

Ability to communicate: Students need to be able to communicate complex statistical methods in basic terms to managers and other audiences and to visualize results in an accessible manner. They must have a clear understanding of ethical standards. Programs should provide multiple opportunities to practice and refine these statistical practice skills.

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.

2014 Curriculum Guidelines for Undergraduate Programs in Statistical Science (PDF Format)

These guidelines were endorsed by the Board of Directors of the American Statistical Association on November 15, 2014. The new guidelines are now available here. Feedback can be sent to Rebecca Nichols, Director of Education, at rebecca@amstat.org.

White Papers

2013-2014 Webinar Series on Undergraduate Statistics

2000 Curriculum Guidelines for Undergraduate Programs in Statistical Science