Biologists meet statisticians: A workshop for young scientists to foster interdisciplinary team work

Life science and statistics have necessarily become essential partners. The need to plan complex, structured experiments, involving elaborated designs, and the need to analyse datasets in the era of systems biology and high throughput technologies has to build upon professional statistical expertise. On the other hand, conducting such analyses and also developing improved or new methods, also for novel kinds of data, has to build upon solid biological understanding and practise. However, the meeting of scientists of both fields is often hampered by a variety of communicative hurdles - which are based on field-specific working languages and cultural differences. As a step towards a better mutual understanding, we developed a workshop concept bringing together young experimental biologists and statisticians, to work as pairs and learn to value each others competences and practise interdisciplinary communication in a casual atmosphere. The first implementation of our concept was a cooperation of the German Region of the International Biometrical Society and the Leibnitz Institute DSMZ-German Collection of Microorganisms and Cell Cultures (short: DSMZ), Braunschweig, Germany. We collected feedback in form of three questionnaires, oral comments, and gathered experiences for the improvement of this concept. The long-term challenge for both disciplines is the establishment of systematic schedules and strategic partnerships which use the proposed workshop concept to foster mutual understanding, to seed the necessary interdisciplinary cooperation network, and to start training the indispensable communication skills at the earliest possible phase of education

Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in Data Science. The group consisted of 25 undergraduate faculty from a variety of institutions in the U.S., primarily from the disciplines of mathematics, statistics and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in Data Science

Towards understanding models for statistical literacy: A literature review.

Despite statistical literacy being relatively new in statistics education research, it needs special attention as attempts are being made to enhance the teaching, learning and assessing of this strand. It is important that teachers are aware of the challenges of teaching this literacy. The growing importance of statistics in today's information world and conceptions and components of statistical literacy are outlined. Frameworks for developing statistical literacy from research literature are considered next. Strengths and weaknesses of the models are considered. Examples of tasks used in statistics education research are provided to explain the levels of thinking. The paper concludes with some implications for teaching and research