Code du cours InfT5057
Crédits 4.50
La quantité totale d'heures en classe120
Nombre de conferences12
Nombre de travaux pratiques et des séminaires24
La quantitē d'heures de travail autonome d'un ētudiant84
Date de l'approbation du cours19.01.2022
Auteur du cours
Līga Paura
Le cours remplacé
InfTM002 [GINTM002]
Manuels
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.
Ouvrages supplémentaires
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.
Périodiques et d`autres ressources d`information
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