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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.

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 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)

Entry re­quire­ments

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

  • Daten­bank­systeme

  • 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 back­of­ with the sub­ject­line "Green­card SBWL Data Science".

Fur­ther In­form­a­tion and Links

[1]­min/wu/h/press/Presse2015/wu­magazin0314.pdf p. 3ff.

[2]­Journ­al1503.pdf#13 p.13ff.