Universität Passau
5973V Lecture: SQL for Data Science - Details
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General information

Subtitle
Course number 5973V
Semester SoSe 24
Current number of participants 296
expected number of participants 30
Home institute Lehrstuhl für Informatik mit Schwerpunkt Skalierbare Datenbanksysteme
Courses type Lecture in category Lehre (mit Prüfung)
Next date Tue., 01.10.2024 10:00 - 11:00 Uhr, Room: (AM) HS 10
Type/Form
Performance record
60 min. schriftliche Klausur
60-minute written examination
SWS
2
Literatur
Antonio Badia: SQL for Data Science - Data Cleaning, Wrangling and Analytics with Relational Databases. Springer 2020.

Bill Karwin: SQL Antipatterns. Pragmatic Programmers, LLC, 2017.

Raghu Ramakrishnan, Johannes Gehrke: Database Management Systems. McGraw-Hill, 3rd edition, 2002.
ECTS points
6

Course location / Course dates

(JUR) HS 14 Fri.. 10:00 - 12:00 (6x)
Friday. 12.07.24 15:00 - 17:00
Friday. 19.07.24 08:00 - 10:00
(ISA) SR 008 Fri.. 10:00 - 12:00 (4x)
(IM) HS 11 Fri.. 10:00 - 12:00 (1x)
(AM) HS 10 Friday. 26.07.24 16:30 - 17:30
Tuesday. 01.10.24 10:00 - 11:00

Fields of study

This information on acceptance for credit of modules for individual degree programmes is not binding; please check the module catalogue at the Faculty of Computer Science and Mathematics to confirm that this module can be counted towards your degree.

Module assignments

Comment/Description

This advanced database class offers a comprehensive understanding of the data life cycle and the potential of SQL in various data analysis tasks. Students explore topics ranging from data loading and cleaning to pre-processing, while mastering relational databases and handling non-traditional data formats such as XML and text. Integration with programming languages like R and Python further enriches students' abilities, enabling seamless interaction with databases and enhancing data analysis workflows. Practical exercises and hands-on experience with MySQL and Postgres databases solidify students' competencies, equipping them with the essential skills to excel in data science and database management roles.