Lecture Series on Practical Data Engineering (Wintersemester 2019/2020)
Lecturer:
Prof. Dr. Tilmann Rabl
(Data Engineering Systems)
,
Prof. Dr. Felix Naumann
(Information Systems)
Course Website:
https://hpi.de/rabl/teaching/current-courses/lecture-series-on-practical-data-engineering.html
General Information
- Weekly Hours: 2
- Credits: 3
- Graded:
yes
- Enrolment Deadline: 01.10.-30.10.2019
- Teaching Form: Vorlesung / Übung
- Enrolment Type: Compulsory Elective Module
- Course Language: English
Programs, Module Groups & Modules
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-K Konzepte und Methoden
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-S Spezialisierung
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-T Techniken und Werkzeuge
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- SCAL: Scalable Data Systems
- HPI-SCAL-K Konzepte und Methode
- SCAL: Scalable Data Systems
- HPI-SCAL-T echniken und Werkzeuge
- SCAL: Scalable Data Systems
- HPI-SCAL-S Spezialisierung
- SECA: Security Analytics
- HPI-SECA-K Konzepte und Methoden
- SECA: Security Analytics
- HPI-SECA-T Techniken und Werkzeuge
- SECA: Security Analytics
- HPI-SECA-S Spezialisierung
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-C Concepts and Methods
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-T Technologies and Tools
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-S Specialization
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-C Concepts and Methods
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-T Technologies and Tools
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-S Specialization
Description
The Lecture Series on Practical Data Engineering will feature presentations by distinguished speakers from industry and academia on the topic of data engineering. We will cover topics on the application of data engineering in industrial setups as well as system level advancements in data engineering research.
Literature
To be announced in the course.
Learning
To be announced in the course.
Examination
Lecture Summary
You have to write a summary for one lecture. Depending on the number of participants, this will be done in groups. Each lecture will be assigned to one person or group.
All summaries will be published on the course website. The summary should give a quick and engaging overview of the talk (10-15 min reading time). We will ask the presenters to review your summary and help you with questions.
Individual Poster Project
You have to prepare an A1 poster, with one of the following contents. You will be able to present your poster at the final session.
Extending technology or methodology presented in one or multiple of the lectures, you should prepare a research project proposal. The poster should highlight goal, problem, and solution of your proposal and the connection to the lecture.
Mark Breakdown
- 50% Lecture summary
- 50% Individual poster project
Dates
To be announced in the course.
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