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.
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.
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.