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OT-ST-WS-10 | Data base skills

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Prof. Dr. Sebastian Maneth


Relational data is ubiquitous and the majority of data is stored in relational database management systems (RDBMS). Anyone who needs to analyse and query large data will come across RDBMS and will need to know how to use them and interface with them. Moreover the value, in terms of scientific insight as well as market advantage, of knowing the ins and outs of the query language SQL can hardly be overestimated. According to a 2017 Stackoverflow Developer Survey, SQL is the second most popular programming language in use.


This course will give a hands-on introduction to relational database management systems (RDBMS). You will learn how to structure your database and how to create tables within an RDBMS. You will learn how to import data from CSV files into your tables. We will use a large bibliography database as our example database. The main part of the course will about learning how to formulate interesting queries in SQL. We will also learn how to display the results of queries using other software (such as Python, R, of gnuplot). The ability to query and display results offers a powerful data analytics platform. Will discuss the limitations of RDBMs and scenarios in which modern NoSQL database systems are able to address and overcome these limitations.


You will know how to structure data into relational tables, how to create such tables in a relational database management system (RDBMS), and how to query the data using the SQL query language. You will have hands-on experience in formulating interesting queries on a large database.

Prior knowledge



No prior knowledge required, other than being able to run “SQLite3” on your device, or how to run “SSH” on your device.


Postponed to:

23.09.2021, 9:00 - 11:00 and 14:00-16:30

24.09.2021, 9:00 - 11:00 and 14:00-16:30





Sebastian Maneth

Prof. Dr. Sebastian Maneth

Heisenberg Professor (full professor) University of Bremen. Sebastian Maneth’s research interest lies in efficient storage and querying technology for semi-structured data.