Data Analytics in Applications

Goal   
Qualifying and empowering students to use Data / Business Analytics in practice.

Prerequisites  
Basic skills in programming with python & jupyter notebooks

Basic Course Structure

Four seminars of (max) four hours each to

  • Teach theoretical basics
  • Provide Data Analytics tools at hand

Team project

  • Data Analytics use case as project study in group work
  • Use cases are developed by the students themselves or given.

The final grade is composed of:

  • Written submission of documentation & digital submission of program code (80 %)
  • Final Presentation / Pitch of the results and the tool (20 %)

Guest Lectures  
Participants of the KI-Lab present challenges, best practices, visions and proposals for   thesis in the field of Data analytics.

Module description

Learning outcomes: After completing the seminar, students will be able to identify data analytics use cases in a structured manner and work on them independently. Students learn:

(i) the identification of valuable and practical data analytics use cases.

(ii) the structured project planning of programming tasks as well as the processing of a data analytics use case in a team using state-of-the-art software.

(iii) the targeted compilation of results and program code.

Content: The lecture is structured according to the CRISP-DM model. The module consists of three sub-modules: In the first sub-module, the necessary theoretical basics are taught in the form of seminars and exercises. Exemplary use cases are presented and the methodology for processing data analytics use cases is taught. In the second submodule, students work on a self-identified or specified use case. Support will be provided in the form of consultation hours. In the last sub-module, students compile the methodology and results of the use case processing in a project report. The results are then presented. The seminar takes place in cooperation with the newly founded TUM KI-Lab, which consists of a consortium of well-known representatives from industry. Exclusive insights into the data analytics applications of the collaborating companies will be provided through guest lectures. Students have the opportunity to establish valuable industry contacts.

Theory: The module consists of 4 seminar units of 4 hours each including exercises in the form of Jupyter notebooks (python). This is followed by independent work on a project in teamwork. During the team project office hours are available for support. Guest lectures from the industry enhance the practical relevance of the seminar.