All Items 8

Teaching the next generation of statistics students to "think with data": Special issue on statistics and the undergraduate curriculum

Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College)

Teaching the next generation of statistics students to "think with data": Special issue on statistics and the undergraduate curriculum
Ensuring that mathematics is relevant in a world of data science

Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College)

The recent growth of data science has been remarkable. Analysts now have rich data and powerful computational tools to help answer important questions. Examples of ways that insights can be wrangled from this information abound in diverse areas. This has led some to dub computational thinking (or fluency) as the "new literacy" on par with writing and quantitative skills. A major unanswered question relates to the role of mathematics in the training of future data scientists. How can we be sure that data science is on a firm mathematical and statistical foundation? In the article, we will consider what courses in mathematics would best prepare future data scientists.

Ensuring that mathematics is relevant in a world of data science
Making progress in a crowded market

Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College)

In their wide-ranging, insightful and provocative paper, Ograjenšek Gal (2016) restate and enunciate our shared goal to help motivate students to learn to use data to provide answers to real-world problems. They also provide specific suggestions about additional ways to create more opportunities for scaffolding and practice of qualitative thinking in our courses and curriculum. I commend them for this guidance—which is wholly consistent with the sentiments of many—including Brown Kass (2009), the INGEnIOuS project, http://www.ingeniousmathstat.org and Cobb (2015). All suggest a broad definition of statistics, encourage efforts to ensure that statistics is a vibrant choice for students and call for major (radical?) changes to our curriculum to support this shift. While there have been major efforts in recent years to adapt our curriculum to provide exposure to the excitement of statistics [see for example several of the rich datasets in Gould, (2010)], students need to be more directly engaged with a broad range of new data-related topics to be successful in our increasingly diverse discipline (Horton, 2016; Ridgway, 2015). The growth of data science poses many challenges and opportunities. As the authors describe, to be relevant in this world awash in data, statistics students need exposure to quantitative and qualitative approaches for the planning and design of studies, conduct and analysis, as well as interpretation and communication of results and implications. I will briefly consider the implications of this paper as well as barriers for implementation of the approaches enunciated in this paper in two distinct areas: (1) our introductory courses as well as (2) our undergraduate programs in statistics.

Making progress in a crowded market
Meeting student needs for multivariate data analysis: a case study in teaching an undergraduate multivariate data analysis course

Wagaman, Amy S. (Department of Mathematics and Statistics, Amherst College)

Modern students encounter large, messy data sets long before setting foot in our classrooms. Many of these students need to develop skills in exploratory data analysis and multivariate analysis techniques for their jobs after college, but such topics are not covered in traditional introductory statistics courses. This case study describes my experience in designing and teaching an undergraduate course on multivariate data analysis with minimal pre-requisites, using real data, active learning, and other interactive activities to help students tackle the material. Multivariate topics covered include clustering and classification (among others) for exploratory data analysis and an introduction to algorithmic modeling.

Meeting student needs for multivariate data analysis: a case study in teaching an undergraduate multivariate data analysis course
R markdown: Integrating a reproducible analysis tool into introductory statistics

Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College)

Nolan and Temple Lang argue that “the ability to express statistical computations is an es- sential skill.” A key related capacity is the ability to conduct and present data analysis in a way that another person can understand and replicate. The copy-and-paste workflow that is an artifact of antiquated user-interface design makes reproducibility of statistical analysis more difficult, especially as data become increasingly complex and statistical methods become increasingly sophisticated. R Markdown is a new technology that makes creating fully-reproducible statistical analysis simple and painless. It provides a solution suitable not only for cutting edge research, but also for use in an introductory statistics course. We present experiential and statistical evidence that R Markdown can be used effectively in introductory statistics courses, and discuss its role in the rapidly-changing world of statistical computation.

R markdown: Integrating a reproducible analysis tool into introductory statistics
Challenges and opportunities for statistics and statistical education: looking back, looking forward

Nicholas J. Horton (Department of Mathematics and Statistics, Amherst College)

The 175th anniversary of the ASA provides an opportunity to look back into the past and peer into the future. What led our forebears to found the association? What commonalities do we still see? What insights might we glean from their experiences and observations? I will use the anniversary as a chance to reflect on where we are now and where we are headed in terms of statistical education amidst the growth of data science. Statistics is the science of learning from data. By fostering more multivariable thinking, building data-related skills, and developing simulation-based problem solving, we can help to ensure that statisticians are fully engaged in data science and the analysis of the abundance of data now available to us.

Challenges and opportunities for statistics and statistical education: looking back, looking forward
Setting the stage for data science: Integration of data management skills in introductory and second courses in statistics

Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College)

Many have argued that statistics students need additional facility to express statistical computations. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science, and applying these to real-world scenarios, we prepare them to think statistically. In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. We believe that the integration of these precursors to data science into our curricula—early and often—will help statisticians be part of the dialogue regarding Big Data and Big Questions.

Setting the stage for data science: Integration of data management skills in introductory and second courses in statistics
Rail trails and property values: is there an association?

Horton, Nicholas J. (Department of Mathematics and Statistics, Amherst College)

Rail trails and property values: is there an association?