Data Science: Data Management and Analytics (DMA)


The module “Data Science: Data Management and Analytics” provides a compact overview of essential concepts, procedures, and technologies for managing, storing, querying, integrating, transforming, evaluating, and visualizing data. Through a mixture of theoretical lessons and practical exercises, students get to know basic procedures and technologies of data management and analytics, enabling them to conceptualize and to implement a suitable data management and analytics approach depending on the particular use case.

The module is offered in two variants:

Module Name Data Science:
Data Management and Analytics
Data Science:
Data Management and Analytics for Information Systems (GOP)
Target Group
Students of Business
in 2nd semester
(start of studies from 2020/21)
Students of Information
in 2nd semester (start of studies from 2020/21)
Recommended Prerequisites
  • Data Science: Data Driven Business
  • Data Science: Datenauswertung
  • Data Science: Statistik
  • Data Science: Data Driven Business
  • Algorithmen und Datenstrukturen (für MT)
  • Knowledge of the R programming language equivalent to „Basiskurs R/RStudio“ in StudOn

Transfer of Knowledge

In the lecture, students learn theoretical and technical fundamentals of modeling, managing, querying, integrating, transforming, evaluating, and visualizing data and understand how their interaction can be used to design and implement a structured data management and analytics process. An accompanying case study illustrates concrete applications of the concepts in an operational context.

Application of Knowledge

For each unit, exercises related to the case study are discussed in a global exercise class, which encourage the application of the theoretical knowledge from the lecture. The tasks are adapted to the two target groups of the lecture to specifically address the different prior knowledge of the respective students. In addition to self-tests in StudOn, which contain comprehension questions on the contents of the respective lecture unit, tutorials are also offered whenever possible, in which the topics of the exercise classes can be further deepened and remaining questions can be clarified.

Implementation of Knowledge

Interactive exercises (e.g. formulating database queries, performing simple calculations, evaluating data sets, etc.) are offered in StudOn for both target groups of the lecture, which are suitable for autodidactic processing. In addition to these simple application tasks, students of the information systems program are also offered the opportunity to apply their knowledge within a semester-long group project in which the technologies introduced during the lectures are applied to manage, integrate, and analyze data sets from real-world business scenarios.

Learning Goals

The aim of the module is to provide a detailed overview of essential concepts, procedures, and technologies of data management, data integration, and data analytics and to understand how these can be used in a corporate context to generate strategically relevant knowledge from data sets of the operative business.

Upon successful completion of the module, students will recognize the strategic relevance of structured data management and analytics for companies. They will be able to design a data management and analytics process aligned with strategic business goals and to implement it using suitable technologies. In addition, students will have a basic technical understanding in the areas of data management and data analytics through the acquired knowledge in SQL, R, and Tableau, which is additionally deepened especially for students of information systems through practice-oriented project work with SQL, web technologies, R, and Tableau.

Additional Hints

The module consists of lectures and exercise classes and concludes with a written exam, which is set separately for students of business studies and information systems to evaluate the acquisition of the respective learning objectives.

The lecture follows a “flipped classroom” approach: with the help of learning videos and supplementary materials (lecture slides, script, and literature), the students prepare themselves for the on-site lectures, which focus on repetition and application of the acquired knowledge to the case study. This part of the module is identical for both target groups.

The exercise classes take place separately for both target groups and consist of materials provided in advance, which are presented and discussed during the on-site appointment. For students of information systems, a voluntary semester-long practical project work is also offered, the successful completion of which leads to a grade improvement for the final exam. In addition to the exercise classes, tutorials are offered whenever possible, where students can ask individual questions and receive support in working through the practice assignments.

Exercise Class Dates

The exercise will take place on the following dates in hybrid mode (i.e., in parallel in presence as well as via Zoom), provided that the School of Business and Economics decides to offer hybrid teaching in the summer term. Independent of this, all classes will also be offered digitally in the form of a Zoom meeting or Zoom webinar. The students select one of the following dates for the exercise class based on their study program. Further details on the exercise classes will be announced shortly before the start of the semester.

Business Studies

  • Thursday, 16:45 – 18:15, LG H5 & Zoom
  • Thursday, 16:45 – 18:15, LG H6 & Zoom
  • Friday, 9:45 – 11:15, LG H5 & Zoom

Information Systems

  • Friday, 11:30 – 13:00, LG H6 & Zoom