2117 Data Science Lab
PD Dr. Ronald Hochreiter, Univ.Prof. Dr. Axel Polleres
Weekly hours
Language of instruction
09/01/20 to 09/14/20
Registration via LPIS
Notes to the course
Day Date Time Room
Wednesday 10/07/20 09:00 AM - 09:45 AM Online-Einheit
Wednesday 10/07/20 10:00 AM - 06:00 PM D5.1.003
Wednesday 10/07/20 10:00 AM - 06:00 PM D5.1.002
Monday 11/23/20 08:00 AM - 06:00 PM Online-Einheit
Tuesday 11/24/20 09:00 AM - 12:00 PM Online-Einheit
Wednesday 11/25/20 08:00 AM - 06:00 PM Online-Einheit
Monday 01/18/21 08:00 AM - 04:00 PM Online-Einheit
Tuesday 01/19/21 08:00 AM - 03:00 PM Online-Einheit

Procedure for the course when limited activity on campus

  • The kick-off will take place in a hybrid mode combining an online sesssion and small group meetings per project:
    • There will be a short introduction for all between 9am and 9:45am using MS Teams. At the end of the introduction for all students in this plenary session, we will assign students to groups, following preferences that will be need to be  submittedby all students in a pre-course assignment.
    • After he online session, we are going to use two seminar rooms in building D5 (D5.1.002 and D5.1.003) between 10am and 6pm to kick-off each individual project group.  The idea is that students of each group get to know their Data Coach and their assigned WU instructor., while we avoid big plenary sessions.  We have reserved big enough rooms, such that we can split up student groups and allow for breaks in between to exchange air. Distance rules and the recommendation for masks apply as per the official measurements in place.
  • Regular Meetings with data Coaches and supervisors per group are expected to boo organised by the students, either online or - given that distance and other maeassurements can be followed  and al particiaptns are ok with it- as physical meetings per project. 
  • The sparring group meetings are expected to run self-organized by the students, either online or - given that distance and other maeassurements can be followed  and al particiaptns are ok with it- as physical meetings in small groups. 
  • The intermediate consultations in the middle of the semester are also planned to be online events in MS Teams in individual slots per project group.
  • As for the final presentations of all teams in the end of the semester with all groups and data coaches attending, whether we can do these final presentations in a lecture room or still have to resort to a virtual mode, is yet to be determined, depending on the situation in January.


The final course of the SBWL Data Science will be conducted in group projects that are introduced in a joint kickoff-workshop together with"Data Coaches" (members of one of the involved institutes and from industry partners). Thereafter, the project teams are formed and each team will have to elaborated, together with their data coach, a concrete project plan for a Data Science project to be conducted over the duration of the semester, involving regular interactions with the data coach and the teachers of the course. It id the objective of this course to develop a front-to-end solution proposal to a practical problem in a team. The data coaches will provide data sets and tools from realworld use cases (from industry or from open data). The coordination will be done in 2 parallel courses, each of which takes over supervision of half of the teams. Each team will consist of 3-4 students.

Learning outcomes

You will learn the following in this course:

  • apply the theoretical knowledge of courses 1-3 of the SBWL in practice
  • work in teams
  • understanding and diving into a concrete problem domain
  • manageing and self-learning new tools used in a practical context
  • working out a project plan and conducting a data science project
  • interaction with a "customer", the data coach, with a realworld analytics problem
  • applying ste-of-the-art data science methods from the scientific literature to realworld data problems

Attendance requirements

Attendance of the plenary introduction session, the intermediate meetings (for individual groups) and the final plenary presentations (all students are expected to be present and give feedback to all the others' presentations) is required. In addition, at least two sparing group meetings - one between introduction and intermediate as well as one between intermediate and final - are mandatory. 

Teaching/learning method(s)

  • Team building
  • Writing a project plan in interaction with the course supervisors and the data coach
  • Regular interaction of the team is expected and should be documented, team roles should be defined in the project plan
  • presentation of the intermediate results and project progress to the supervisors in an intermediate meeting, along with a draft project report
  • presentation of project results in front of the plenary in the end.
  • At least two presentations and discussions with the assigned sparing group. The preparation of minutes is mandatory.
  • Writing a project end report that documents the outcomes and allows others to understand your approach and re-implement/re-evaluate your results, the project report shall consist of a practical and a theoretical part, where the approach and solution is described in a reproducible manner, but also scientific methods and approaches and the litereature which explains them is surveyed.


We will assess the following partial contribtions for grading the course:
 - Projekt proposal (10%)
 - Intermediate consultation: assessment of intermediate results and progress, steering (20%)
 - Sparing group minutes (10%)
 - Final presentation (25%)
 - Final Report (35%)
for each result we will assess the group result and the individual contribution of the team members, i.e., it is ok if not all team members do presentations, but then it should be made clear from the project plan and report how the work was split and who contributed what.

We emphasize that this course as the final and most challenging course of the SBWL shall both teach you to apply what you leanrt so far, but also will require self-driven, motivated work and probably the acquisituion of even new skills to achieve excellent results. As we work with real companies, we will do our best to support you but we also expect the teams' ambition to produce and explore creative and innovative solutions by doing their own research, which we can only guide as instructors. Particularly, we hint again that, with a value of 4 ECTS the course amounts to at least 100hrs of work invested per team member into the project which should be justifiably presented and planned in the team's project plan.

Prerequisites for participation and waiting lists

Successful conclusion of the course 1 of SBWL Data Science.

Please be aware that for all courses in this SBWL registration is only possibly for students who successfully have completed the entry exam or admission (Access to the  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.

Recommended previous knowledge and skills

We strongly recommend you should have completed courses 1-3 of the SBWL before this course.

Last edited: 2020-09-13