Data Ethics and Identity
Included in the Master's Programme in Data Science, a course on Data Ethics and Identity
The University of Salzburg offers a Master's Programme in Data Science. This includes a mandatory course on Data Ethics and Identity as part of the compulsory module in Module Statistical Practice, Case Studies, Ethics.
To learn more about the course, see the study guide of the course or contact the University.
More about the course in
During the course students will focus on:
- Privacy and identity of people in their relation to data that are being obtained, stored, or analyzed
- Ethical questions surrounding the use of data
More about the Master's in Data Science
After four very successful years of the first Data Science master’s programme in Austria, the present curriculum marks the step from a German into an English program. This internationalization step has also been used to update the curriculum and to make adaptations based on the experiences of the previous years.
The master’s programme in Data Science centers around how to make sense of data effectively, knowledgeably, and responsibly, in order to advance knowledge and insight. Providing solutions to the challenges associated with the relatively new field of Data Science is of vital importance for enterprises, governments and other organization, as well as for individuals. In addition, generating, modeling, analyzing, and interpreting dat a is central in today’s scientific endeavor at universities and other research institutions.
The skills and knowledge needed to tackle today’s Data Science challenges, and to develop meaningful solutions, require more than what is provided in traditional curricula in statistics and computer science. Today’s Data Scientist has to develop a holistic view on data from often rather heterogeneous sources, scrutinize critically, analyze using adequate statistical methods, extract relevant information, and correctly interpret the results obtained. On a technical level, the data deluge requires understanding and experience regarding large and often distributed systems for data storing and processing. Making sense of these data encompasses numerous activities, from finding and organizing the data to evaluating their quality, exploratory data analysis, modeling, analyzing, and interpreting the data, to an understandable presentation of results. The Data Science curriculum represents a bridge from raw data to information, from information to knowledge, and from knowledge to making well-grounded decisions.
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