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Statuss(Aktīvs) Izdruka Arhīvs(0) Studiju plāns Vecais plāns Kursu katalogs Vēsture

Kurstitel
Kurs-Code InfT5057
Kreditpunkte (ECTS) 4.5
Stundenzahl insgesamt (im Auditorium) 121.5
Vorlesungen (Stundenzahl) 12
Stundenzahl fŅr Seminare und praktische Arbeitsaufträge 24
Selbststandige Arbeit des Studenten (Stunden) 84
Bestätigt am (Datum) 19/01/2022
 
Kurs ausgearbeitet von (Lehrkraft)
, Līga Paura

6979@@Priekšzināšanas
Kursam priekšzināšanas nav nepieciešamas
 
Ersetzte/r Kurs/e
InfTM002 [GINTM002]
Zur einfŅhrenden LektŅre empfohlen
1.Kirk A. Data visualisation: a handbook for data driven design. Los Angeles: SAGE, 2019. 312 p.
2. Corr L., Stagnitto J. Agile Data Warehouse Design: collaborative dimensional modeling, from Whiteboard to Star Schema. UK: Decision Press, 2014. 304 p.
3.Arhipova I., Balina S. Statistika ekonomikā un biznesā: risinājumi ar SPSS un MS Excel: mācību līdzeklis. Rīga: Datorzinību centrs, 2006. 359 lpp. 4.Kabacoff R. I. R in action: data analysis and graphics with R. Second edition. Shelter Island, NY: Manning, 2015. 579 p.
Weiterfuhrende Literatur
1.Data science & big data analytics: discovering, analyzing, visualizing and presenting data. EMC Education Services. Indianapolis, IN: John Wiley and Sons, 2015. 410 p.
2.Advanced Analytics with Power BI: Microsoft. Pieejams: https://www.arbelatech.com/insights/white-papers/advanced-analytics-with-power-bi 3.Gujarati D. N. Basic econometrics. 3rd ed. New York [etc.]: McGraw-Hill, Inc., 1995. 838 p.
Zur LektŅre vorgeschlagene Zeitschriften
1.European Journal of Management and Business Economics: ISSN 2444-8451 Elsevier data base 2.Journal of Data Analysis and Information Processing: ISSN Online: 2327-7203. Pieejams: www.scirp.org/journal/jdaip