SBWL Data Science
SBWL „Data Science“
The ever growing economic significance in terms of efficient processing and analysis of Big Data for businesses, has led to a research area commonly labelled as “Data Science”, which is getting more and more attention in both academia and industry (cf. also the following articles published in the WU-Magazine  and in the Journal of the Austrian Computer Association (OCG) ).
In response to these global trends WU takes an interdisciplinary, cross-department approach by providing this new SBWL for Bachelor students.
Many departments of the WU are already focusing on Data Science in their research. Among these, the follwing are involved in the new SBWL:
Department of Information Systems & Operations (Institute for Information Business – Prof. Polleres, Prof. Mendling, Institute for Information Systems and New Media – Prof. Neumann, Institute for Production Management – Prof. Mild)
Department of Finance, Accounting and Statistics (Institute for Statistics and Mathematics - Prof. Frühwirth-Schnatter, Prof. Hornik, Ass. Prof. Hochreiter)
Department for Marketing (Institute for Interactive Marketing & Social Media – Prof. Abou Nabout, Institute for Service Marketing and Tourism – Prof. Reutterer)
Department of Business, Employment, and Social Security Law (Institute for Information Law and Intellectual Property Law– Prof. Winner, Ass. Prof. Clemens Appl)
Structure & courses
Students will get an interdisciplinary overview of the fundamentals of data science as well as hands-on approach of newly developed data processing and analysis techniques through working on different projects. To ensure a broad knowledge of the different perspectives of data science, this SBWL will focus on fundamentals of process-specific (1LV), statistical-analytical (1LV), and legal (1LV) essentials. Furthermore, the SBWL focuses on end-to-end solutions of data-specific issues in business administration domains like Marketing and Supply Chain Management (1LV), and with a final group-project (so called Data Science Lab) (1LV).
Since the topic and the ever growing exchange of knowledge are received in a global context, the default language of the SBWL will be English.
SBWL 1: Data Processing 1 (PI2.0)
Data Formats and standards
Database systems and data storage
Data cleansing: methods for data preprocessing and improving the data quality
Tools and algorithms for data transformation
Learning objective: Gaining fundamental knowledge for dealing with different data formats and in using methods and tools to integrate data from various sources.
SBWL 2: Data Analytics (PI2.0)
Revision of mathematical & statistical fundamentals
Algorithms for data analysis and data mining
Data analysis tools (R)
Visualizing of data analysis results
Learning objective: Being able to work with and understand the algorithms of data analysis processes and their fundamentals, as well as working with tools to analyze and visualize the data.
SBWL 3: Data Processing 2: Scalable data processing, legal & ethical foundations of data science (PI 2.0)
Scalable Data Processing Frameworks and Paradigms (e.g. Hadoop, Map Reduce, and other Scalable Data Processing Models)
Public Data vs. Open Data
Handling of different licenses
Legal Framework (Data Privacy Act, Freedom of Information Act)
Learning objective: Scalable handling of big data, understanding legal fundamentals and ethical frameworks in dealing with data in an international context.
SBWL 4: Applications of Data Science (PI 2.0)
Examples of application and specific algorithms from concrete use-case domains,i.e.
Data Science in Marketing (Data-driven Advertising, Digital Marketing and Social Media), including specific methods to analyze data such as social network analysis but also learning new technologies in the field of Marketing like real-time bidding, Store Trek – 3D Shopping, second screen, wearable devices and smart metering)
Data Science in Supply Chain Management (demand planning & forecasting, demand-modelling, inventory management, revenue management)
Data Science in Process Management (fundamentals of process analysis, event-driven data of automatic process discovery, process conformance analysis)
Learning objective: Knowing the fields of application, specific algorithms and methods of data analysis as well as the scalable processing in specific areas of business administration with connection to other courses.
SBWL 5: Data Science LAB (PI 2.0)
The final course of the SBWL is focusing on group projects. The different projects will be presented in a joint workshop with data coaches (members of the involved institutes and industry partners). The student groups have the chance to work on practical problems front-to-end while interact actively with the data coaches on their topic. The data coaches will offer real life data sets (derived from operational applications or an open data area) and tools. We have been in touch with several companies to provide a broad spectrum of perspectives and examples, e.g. also in the framework of the Internet-Offensive. The coordination of the projects will take place within 2 parallel courses with one coordinator each, who can supervise 4-5 groups with 3-5 students.
To successfully succeed students will have to:
Attend the Kickoff-Workshop: Presentation and distribution of the topics through the data coaches, distribution in groups
Submitting a problem definition of the proposed use case and add the description of the personal role during the project (pass/fail)
Pre-presentation of the group (graded 1-5)
Final report of the group including individual description of the individual performance (graded 1-5)
Precondition for the participation at the SBWL is the willingness to work with “hands-on” approaches using data algorithms to find solutions for different problems within the area of business administration. To meet this requirements students either have to completed previous courses in the fields of database systems, fundamentals of programming or introduction to statistic, or pass a preliminary test which will be connected to an introductory tutorial.
Please find detailed information on the entry tutorial and the material relevant for the entry test in VVZ under "Einstieg in die SBWL: Data Science".The same criteria as in WS 16/17 apply.
Please be aware that for all courses in this SBWL registration is only possibly for students who successfully have completed the entry course (Einstieg in die SBWL: Data Science).
Note that for courses within the SBWL "Data Science" we can only accept students enrolled in one of WU's bachelor programmes who qualify for starting an SBWL; particularly, we cannot accept students from other courses and programmes enrolled at WU as 'Mitbeleger' only.
Students who achieved a grade of "Sehr Gut (1)" in two out of the following courses
Grundzüge der Programmierung
Einführung in die Statistik
are automatically qualified for the SBWL, but should nevertheless complete the entry exam, since it will serve as the first partial assessment for the SBWL course "Data Processing 1".
ATTENTION: Students who want to make use of this "Greencard-Option" should send a confirmation (Sammelzeugnis) of the nessesary grades in advance to firstname.lastname@example.org with the subjectline "Greencard SBWL Data Science".