Data and Data Science: Making Sense of the World

The Proceedings of the 12th International Congress on Mathematical Education, 2015

The American Statistician, 2018

Demand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the Statistics curriculum. The unifying recommendation is computing should play a more prominent role. We strongly agree with this recommendation, but advocate the main priority is to bring applications to the forefront as proposed by Nolan and Speed (1999). We also argue that the individuals tasked with developing data science courses should not only have statistical training, but also have experience analyzing data with the main objective of solving real-world problems. Here, we share a set of general principles and offer a detailed guide derived from our successful experience developing and teaching a graduate-level, introductory data science course centered entirely on case studies. We argue for the importance of statistical thinking , as defined by Wild and Pfannkuck (1999) and describe how our approach teaches students three key skills needed to succeed in data science, which we refer to as creating , connecting , and computing. This guide can also be used for statisticians wanting to gain more practical knowledge about data science before embarking on teaching an introductory course.

Journal of Statistics Education

This article describes a collaborative project across three institutions to develop, implement, and evaluate a series of tutorials and case studies that highlight fundamental tools of data science-such as visualization, data manipulation, and database usage-that instructors at a wide-range of institutions can incorporate into existing statistics courses. The resulting materials are flexible enough to serve both introductory and advanced students, and aim to provide students with the skills to experiment with data, find their own patterns, and ask their own questions. In this article, we discuss a tutorial on data visualization and a case study synthesizing data wrangling and visualization skills in detail, and provide references to additional class-tested materials. R and R Markdown are used for all of the activities.

Promoting Understanding of Statistics about Society IASE Roundtable Conference

Making sense of data is complex, and the knowledge and skills required to understand "Big Data" - and many open data sources - go beyond those taught in traditional introductory statistics courses. The Mobilize project has created and implemented a course for secondary students, Introduction to Data Science (IDS), that aims to develop computational and statistical thinking skills so that students can access and analyze data from a variety of traditional and non-traditional sources. Although the course does not directly address open source data, such data are used in the curriculum, and an outcome of the project is to develop skills and habits of mind that allow students to use open source data to understand their community. This paper introduces the course and describes some of the challenges in its implementation.

2018

Within the framework of a design-based research project, computer science educators and statistics educators at Paderborn University designed a pilot course on the subject of data science and big data. It addresses upper secondary students and was realized by weekly sessions (three hours) over seven months. The whole course that is intended to introduce upper secondary school students to the field of data science consists of four modules. In module 1, the learners are introduced into the basics of statistics and big data and it aims at developing their data competence and data awareness. In the sec- ond module, learners are introduced to machine learning and programming based, among others, on examples from module 1. In the third and fourth module, learners can apply their knowledge gained in modules 1 and 2 and will work in small groups on real and meaningful data science projects. In this paper, we want to concentrate on the statistics components, especially of module 1, and we wi...

2010

Much has been written about the importance of understanding quantitative data in today's society (Briggs, 2002; Madison, 2002; Scheaffer, 2001; Steen, 2001). As implied in the quote above, understanding of most of today's societal concerns requires the use and interpretation of data (to the list above we can add concerns such as water and food scarcity, global warming, the use of genetically modified crops, etc.).

Springer eBooks, 2023

Demand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the Statistics curriculum. The unifying recommendation is that computing should play a more prominent role. We strongly agree with this recommendation, but advocate the main priority is to bring applications to the forefront as proposed by Nolan and Speed (1999). We also argue that the individuals tasked with developing data science courses should not only have statistical training, but also have experience analyzing data with the main objective of solving real-world problems. Here, we share a set of general principles and offer a detailed guide derived from our successful experience developing and teaching a graduate-level, introductory data science course centered entirely on case studies. We argue for the importance of statistical thinking, as defined by Wild and Pfannkuch (1999) and describe how our approach teaches students three key skills needed to succeed in data science, which we refer to as creating, connecting, and computing. This guide can also be used for

2014

Statistics students need to develop the capacity to make sense of the staggering amount of information collected in our increasingly data-centered world. Data science is an important part of modern statistics, but our introductory and second statistics courses often neglect this fact. This paper discusses ways to provide a practical foundation for students to learn to "compute with data" as defined by Nolan and Temple Lang (2010), as well as develop "data habits of mind" (Finzer, 2013). We describe how introductory and second courses can integrate two key precursors to data science: the use of reproducible analysis tools and access to large databases. 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 the era of big data.

The American Statistician, 2015

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.

Procedia Computer Science, 2017

The growing importance of data competency implies that adequate Data Science course offerings should be available to all students regardless of their interests, backgrounds and intended major. This paper describes the design and implementation of a course targeted at a non-technical audience and centered on data science literacy, with a focus on collecting, processing, analyzing and using data. The objective is through general education to prime students at an early stage of their education for the changes in the data-driven society and to provide them with skills to navigate capably in today's data-rich world. Our experience and evaluation results indicate that it is realistic for a diverse population of undergraduate students to acquired data science literacy and practical skills through a general education course.