Data Science

General data

Course ID:	2600-DS-OG
Erasmus code / ISCED:	04.2 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0410) Business and administration, not further defined The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Data Science
Name in Polish:	Data Science
Organizational unit:	Faculty of Management
Course groups:	General university courses General University Courses in Faculty of Management General university courses in the social sciences
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Type of course:	general courses
Prerequisites (description):	The course is part of Integrated Program for University of Warsaw (ZIP), co-funded by EFS, part of PO WER, pathway 3.5
Mode:	Remote learning
Short description:	Discussion of the purpose / subject of the studied phenomenon, a set of tools (procedures, software and hardware) and the basics of knowledge for using Big Data refining. It enables the construction of a past, current and predicted picture of the phenomenon under study.
Full description:	Discussion of the purpose / subject of the studied phenomenon, a set of tools (procedures, software and hardware) and the basics of knowledge for using Big Data refining. It enables the construction of a past, current and predicted picture of the phenomenon under study. The course is divided into modules covering specific aspects of Data Science. Every term will be explained based examples and/or case studies.
Bibliography:	All materials will be provided to the students during the course in traditional and electronic form.
Learning outcomes:	Upon the completion of the course, a student can: • Select tools for the practical implementation of information refining • Identify information sources and collect information • Recognize text entries available in a non-text format • Harness potential of AI for data refining • Create a model of the examined process
Assessment methods and assessment criteria:	Several tasks with team discussions and assignments

This course is not currently offered.

Course descriptions are protected by copyright.
Copyright by University of Warsaw.