We would like to inform that SGH online lectures will start at e-sgh platform on February 17 or 24, 2025. Detailed information is available after logging at the specific course presentation sites.
We hope you will enjoy e-learning.
SGH full-time or extramural students can choose from more than 50 online courses. Online lectures are organized on the e-learning authoring platforms, which are used by several thousand users.
Niezbędnik e-sgh is a part of the e-learning platform www.e-sgh.pl which allows SGH lecturers to self-manage didactic materials. The application supports lecturers in delivering classes as well as enables them to communicate with their students.
Educational programme at SGH includes a few blended learning courses. The teaching process of such a lecture is realized as a complementary form (traditional classes in conjunction with e-learning).
Supplementary materials for basic courses are prepared as an online modules. The content of those modules extend the study programme of the basic courses, including English and German language.
13-02-2025
We would like to inform that SGH online lectures will start at e-sgh platform on February 17 or 24, 2025. Detailed information is available after logging at the specific course presentation sites.
We hope you will enjoy e-learning.
01-10-2024
We would like to remind you, that SGH students are required to pass three obligatory e-learning trainings.
The e-workshops will be activated on 1st October 2025. To take the trainings, students should log into www.e-sgh.pl and make themselves acquainted with all the training materials and pass the obligatory tests.
2025-04-02
In an environment where information and technology solutions are increasingly important and data volumes are growing rapidly, enterprises need to continually adapt their data, information, and resulting knowledge management strategies to remain competitive in the markets. The application of advanced data mining and data science techniques, such as Recurrent Neural Networks (RNNs), is becoming not only a necessity but also a pivotal element of an organisation’s success. The objective of this paper is to analyse how RNN models are used to forecast company stock prices, particularly the role of such models in enhancing data, information, and resulting knowledge management processes in organisations.