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 [1] and in the Journal of the Austrian Computer Association (OCG) [2]).

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.

Admission - Guiding Principles:

Our admission process and criteria are guided by the following principles: based on experiences from prior runs of the SBWL, we have found out that, on the one hand average grades are highly correlated with success in our SBWL, whereas there are also exceptions to this rule, in the sense that students who don't perform well in prior lectures do excellently in our entry exam. We value prior expertise in data-oriented aspects in your studies, which is why we offer special "Green Cards" for students performing extraordinarily well in selected respective courses.

Following these guiding principles, the admission process is as follows.

Admission - Process:

  1. With applying to the course "Access to Specialization: Data Science" you consent to us requesting from the vice rectorate of teaching your grades transcript (Sammelzeugnis). 

  2. The course "Access to Specialization: Data Science​​​​​​​" consists of two Tutorial sessions (attendance voluntary) and an obligatory (for all, even for those who would qualify for a green card or with excellent average grades) entry exam, which will be held as an online-exam.

  3. Places in the SBWL (overall at maximum 60) will be assigned as follows:

  4.  

  • 10 spots are reserved for the top overall average grades, where in case of ties we take the average grades of the GreenCard courses (see below) as ranking/tie breaker.

  • a maximum of 10 further spots are reserved to students qualified for a GreenCard; in case of more GreenCard qualified applicants, the overall average grade we take the average grade as ranking/tie breaker.

  • The remaining spots are filled by the results of the entry exam, where again the overall average grades will be used as a tie breaker in case of ties.

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)

  • Ethics

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 goal of this final course is to solve a practical use case in a joint group of 3-4 students, with a focus on applying what you learnt in the courses 1-3 by conducting a real Data Science project as a teal (project planning, interaction with a "customer", teamwork and team coordination). The data coaches will offer real life data sets (derived from operational applications or an open data area) or also tools. The coordination of the projects will take place within 2 parallel courses by the respective course coordinators, who supervise 7-8 groups with 3-4 students. In joint kickoff and final presentation sessions the projects are first introduced and teams are formed, whereupon - in the end of the semester the results of all projects are being presented and discussed in the plenary.

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)

GREENCARD

Students who completed all of the following courses and achieved an average grade of 1,5 or lower across the three courses:

  • BaWISO/WiRe (average grade 1,5 among these 3 courses):

    • Grundzüge der Programmierung/Algorithmisches Denken und Programmierung,

    • Datenbanksysteme/Data and Knowledge Engineering,

    • Einführung i.d.Statistik

  • BBE (average grade 1,5 among these 3 courses):

    • Quantitative Methods 1

    • Quantitative Methods 2

    • Business Analytics 2

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 datascience@ai.wu.ac.at with the subjectline "Greencard SBWL Data Science".

These requirements apply to admissons from SS 2020 onwards.

Further Information and Links

[1] http://www.wu.ac.at/fileadmin/wu/h/press/Presse2015/wumagazin0314.pdf p. 3ff.

[2] http://www.ocg.at/sites/ocg.at/files/medien/pdfs/OCG-Journal1503.pdf#13 p.13ff.

We thank our Date Science Lab partners from past semesters

  • Andritz AG

  • IBM Client Innovation Center Austria

  • IBM Österreich

  • KDZ - Zentrum für Verwaltungsforschung

  • LKW Walter Internationale Transportorganisation AG

  • Oesterreichische Nationalbank

  • Ondewo GmbH

  • Project Phoenix Investments GesbR.

  • PS Quant OG

  • PwC PricewaterhouseCoopers Wirtschaftsprüfung und Steuerberatung GmbH

  • ZAMG Zentralanstalt für Meteorologie und Geodynamik

Contact

datascience@ai.wu.ac.at