Universität Bielefeld

© Universität Bielefeld

Bielefeld  Center
for  Data  Science

BiCDaS Teaching

Data is becoming more and more important and not only in science. Data and skills in handling data pervade basically every aspect of the professional world.

Beginning in the winter term of 2018/19, Bielefeld University is offering a master degree programme in Data Science. Highly skilled data professionals are in high demand on the job market and will be so for the foreseeable future. Bielefeld University fulfils its social obligation by educating students to become Data Scientist while also preparing them for a professional world that sometimes expects Data Scientists to perform miraculous deeds.

Even before Data Science became a topic, Bielefeld University already offered a master's degree programme in Statistical Science which provides an in-depth education on classical and cutting-edge statistical methods and their application. This programme attracts many highly talented, analytically minded students each year.

Beyond Data Science as a profession, knowledge and skill in handling data is nowadays advantageous - if not strictly required - in many different fields. That is why Bielefeld University has started an initiative aimed at sharpening the focus on data handling within various disciplines. This is a skill set we call Data Literacy.

Currently Bielefeld University is evaluating and improving Data Literacy teaching for Bachelor degree courses in a step-wise process which is supervised by the coordinator Data Literacy Education.

BiCDaS supports data science and data literacy teaching and is developing a pool of teaching materials. These are either shared by other teaching staff at Bielefeld University or public domain third party materials. We only use materials you can freely use and adapt for any university teaching purpose. You might be required to give proper attribution, however.

Find out More About Teaching@BiCDaS!

 Master's Degree in Data Science

Starting in the winter term 2018/19 Bielefeld University offers a master's degree program in Data Science.

At Bielefeld, Data Science is being taught in a highly interdisciplinary fashion. During a talk in 2017, Michael I. Jordan (Jordan, 2017) identified two complementary ways of thinking, both of which are crucial to Data Science:

a) computational thinking, which refers to algorithmic considerations and computational complexity, as usually taught to computer scientists

b) inferential thinking, which allows researchers to identify and mathematically investigate problems of interest, as commonly taught in statistics programs

The Technical Faculty and the Faculty of Business Administration and Economics provide the necessary expertise in these areas and contribute equally to the Data Science master program, further supported by the Faculty of Mathematics.

Besides a certain mindset and a profound methodical repertoire, Data Scientists need excellent communication skills to present their findings. The ability to communicate results in a comprehensive yet effective manner to a general audience is absolutely crucial for Data Scientists and is, hence, a strong focus in Data Science education.

 Master's Degree in Statistical Science

The MSc in Statistical Science is aimed at students who have a keen interest in empirical and statistical studies and a basic understanding of empirical methodology.

The degree gives interested students the opportunity to consolidate and deepen their knowledge in the field of statistics and empirical methods at an advanced level. The programme focusses on interdisciplinary training by bringing together a variety of important fields of study and methodologies, thus stimulating theoretical and application-oriented content. Students will gain in-depth knowledge of statistics in a wide range of applications and methods. The degree is offered jointly by the faculties of sociology, business administration and economics, psychology, and mathematics, and consists of courses offered by these faculties. Graduates will receive the academic title of Master of Science (MSc) in Statistical Science.

 Data Literacy Education

Various courses and seminars exist that are aimed at equipping students with data competencies in their respective fields. Here is an continually updated list of such offerings at Bielefeld University .

Marketing and Data Science

Ziel der Veranstaltung "Marketing and Data Science" ist es, Methoden und Techniken des Data Mining zu vermitteln und deren Funktionsweise anhand verschiedener Problemstellungen und Beispiele aus Marketing und Marktforschung zu verdeutlichen. Ein Schwerpunkt liegt dabei u.a. auf Klassifizierungsverfahren aus dem Bereich des Maschinellen Lernens. Des Weiteren wird auf Methoden zur Gewinnung und Aufbereitung unstrukturierter Daten im Rahmen des Web Mining eingegangen. Darüber hinaus wird gezeigt, wie die vorgestellten Methoden mittels frei verfügbarer Software eigenständig angewendet werden können.

Data Science @ RCM²-Colloquium

In January 2018, the Research Centre for Mathematical Modelling (RCM²) started a series of talks on Data Science.

Projects in Digital Humanities: Museum Digital

Die Digitalisierung der Gesellschaft ? und vor allem der Wissensgesellschaft ? stellt die Vermittlung von Wissensbeständen und Fragen der Authentizität und Verlässlichkeit von Informationen vor neue Herausforderungen. Gerade die Geisteswissenschaften und insbesondere die Geschichtswissenschaft beschäftigen sich in ihrem Kern mit der Produktion von verlässlichen Wissensbeständen für die Gesellschaft. Das Wissen um die Vergangenheit spielt in vielerlei Hinsicht eine zentrale Rolle in politischen, ideologischen und gesellschaftlichen Diskussionen. Sie spielt eine nicht zu unterschätzende Rolle auch bei Fragen der Wissensvermittlung in und durch Museen. Das Projektseminar will im ersten von zwei Semestern Grundfragen der digitalen Geschichtswissenschaft klären und am Beispiel verschiedener Objekte aus dem Mindener Museum Konzepte digitaler Vermittlung im Museum entwickeln. Dazu wird es im Rahmen der wöchentlichen Veranstaltung verschiedene Workshops mit Expert*innen geben. Die Zusammenarbeit mit dem Mindener Museum erlaubt es, dass im zweiten Semester diese digitalen Vermittlungskonzepte für die Gestaltung einer Ausstellung in Minden umgesetzt werden können.

Communication with the Blackbox: Visualization and Explanation in the Interaction with Algorithms

Today's main challenge in algorithmic data processing and in particular in the Digital Humanities is being able to present the result of digital processing in a humanly readable way. One answer is visualization. The results are not communicated and not explained, they are shown. The differences identified by algorithms are translated into spatial configurations that transform the complex topology of digital processing into two-dimensional images that can be interpreted (if the interpretation succeeds).

Visualization, which is not a typical humanities tool, is borrowed from sciences that use it for analytical purposes. But now the Digital Humanities use visualization to make algorithmic processing meaningful for human beings. We will observe and discuss this trend and its forms in different projects, highlighting the advantages and possible traps of its suggestive intuitive connections. We will focus on the role of interpretation in digital procedures.

Webdaten & Webscraping

In den Sozialwissenschaften nimmt die Relevanz der digitalen Sozialforschung stetig zu. Das Seminar knüpft an diese Entwicklung an und führt die Studierenden anwendungsorientiert in den Umgang mit Webdaten ein. Hierbei werden sowohl grundlegende Techniken der webbasierten Datenerhebung (u.a. das Scrapen von Social Media-Daten auf Twitter und YouTube, Forenbeiträgen oder Tabellen) als auch methodische Kompetenzen zur Aufbereitung und Auswertung dieses neuartigen Datentyps vermittelt. Darüber hinaus werden neben ethischen Fragen, die in diesem Kontext vermehrt auftreten, auch Mehrwert und Limitationen von Webdaten diskutiert und kritisch reflektiert.

Aufbauend auf den Seminarinhalten führen die Studierenden abschließend ein eigenes Projekt mit selbst gewählter Forschungsfrage durch, in dessen Rahmen eigenständig Webdaten erhoben, aufbereitet und analysiert werden sollen.

Research Data Management Seminar

Research data is the foundation of scientific knowledge. Today, in all disciplines large amounts of data are created, mostly in electronic form. Research data management addresses the question, how to make best use of this data. In this seminar we will get to know strategies and tools for efficient documentation, back-up, long-term archiving, publication, retrieval and re-use of research data. Based on real-life case studies from the participants' disciplines, these will be applied and tested. The foundations of good scientific practice and the ideas of Open Science will be discussed. In addition, legal aspects of privacy protection and intellectual property issues will be presented.

 

PEP Seminar "Einführung in das Forschungsdatenmanagement"

Forschungsdaten werden mit großem Aufwand produziert und gehören deshalb zu den wertvollen Gütern von wissenschaftlichen Einrichtungen. Es gehört zur guten wissenschaftlichen Praxis Forschungsdaten als transparente Grundlage von wissenschaftlichen Aussagen möglichst frei zugänglich und langfristig verfügbar zu machen.