Код курса EkonD121

Кредитные пункты 6

Общее количество часов162

Kоличество часов семинаров и практических занятий64

Количество часов самостоятельной работы студента98

Дата утвеждения курса23.11.2022

Разработчик курса

author

Pēteris Rivža

Учебная литературa

1. Dunbar S. R. Mathematical Modeling in Economics and Finance: Probability, Stochastic Processes, and Differential Equations (AMS/MAA Textbooks). ‎ American Mathematical Society. 2019. 232 p.
2. Bandeviča L. Matemātiskā modelēšana ekonomikā un menedžmentā (Teorija un prakse): Mācību grāmata augstskolām. 3.izd. Rīga: SIA Izglītības soļi, 2009. 443 lpp. (ESAF bibliotēka)
3. Saaty T. L. Mathematical Principles of Decision Making (Principia Mathematica Decernendi). First Edition. RWS Publications, 2009. 531 p.
4. Sistēmdinamika vides inženierzinātņu studentiem. A. Blumbergas redakcijā. Rīga: RTU, 2010. 318 lpp.

Дополнительная литература

1. Hannon B., Ruth M. Dynamic Modeling. 2nd edition. Springer, 2001. 428 p.
2. Hannon B., Ruth M. Modeling Dynamic Biological Systems. New York: Springer 2008. 395 p.
3. Pituch K.A., James P. Stevens Applied Multivariate Statistics for the Social Sciences: Analyses with SAS and IBM's SPSS. Taylor & Francis, 2016.
4. Härdle W. K., Simar L. Applied Multivariate Statistical Analysis. Switzerland AG.: Springer Nature, 2020.
4. Mittelbach A, Fischlin M. The Theory of Hash Functions and Random Oracles An Approach to Modern Cryptography. Cham:Springer, 2021. 788 p.
5. van Oorschot P. C. Computer Security and the Internet Tools and Jewels from Malware to Bitcoin. Cham: Springer, 2021. 446 p.
6. Beaver K. Hacking For Dummies. 6th Edition. 2018. 416 p.(DSK bibliotēka)

Периодика и другие источники информации

1. European Journal of Operational Research. ISSN: 0377-2217.
2. Journal of Economic Dynamics and Control. ISSN: 0165-1889.
3. Mathematical and Computer Modelling of Dynamical Systems. ISSN: 1387-3954.
4. SPSS Statistics for Dummies. 3rd Edition. pdf. Pieejams: https://itbook.download/topic/f1604953eaaf43ce9f6033047f8a8556
5. Multivariate Analysis. Pieejams: https://www.stat.auckland.ac.nz/~balemi/Multivariate%2520Analysis.ppt+&cd=1&hl=lv&ct=clnk&gl=lv