Academic year/semester: 2024/25/2

ECTS Credits: 4

Available for: Only for the faculty’s students

Lecture hours: 0
Seminarium:0
Practice: 0
Laboratory: 3
Consultation: 0

Prerequisites: none

Course Leader: Attila Ritzl

Faculty: John von Neumann Faculty of Informatics, 1034 Budapest, Bécsi út 96/b

Course Description:
Goal: Within the scope of the online subject, students will learn about the SAP Analytics
Cloud report creation software, the different steps, real business cases, problems,
operating models and roles.
Course description: Introduction to the world of cloud-based business intelligence; Data environment;
connection types; basics of data modeling; Report creation I. – Analytics Designer;
Story; Data Analyzer; Self - Service; Making a report II. – SAP Analytics Cloud
report types; BI Admin role – management of housekeeping; monitoring; other BI
roles; Life-cycle management; Decision support - using artificial intelligence; User
Experience (UX) trends; Financial planning; what-if cases; General recommendations
for best performance; example analysis; documentation research; BI consulting;
planning; development; and maintenance everyday questions; Market trends; players;
opportunities; outlook.

Competences:
ERP systems, BI

Topics:
1. Introduction to the world of cloud-based business intelligence
2. Data environment, connection types, basics of data modeling
3. Report creation I. – Analytics Designer, Story, Data Analyzer, Self - Service
4. Making a report II. – SAP Analytics Cloud report types
5. BI Admin role – management of housekeeping, monitoring, other BI roles
6. Life-cycle management
7. Decision support - using artificial intelligence
8. User Experience (UX) trends
9. Financial planning, what-if cases
10. General recommendations for best performance, example analysis,
documentation research
11. BI consulting, planning, development, and maintenance everyday questions
12. Market trends, players, opportunities, outlook
13. Test
14. Retake test

Assessment: Participation at lessons is mandatory. Signature cannot be assigned to students who missed more than 30% of lessons. During the semester, students can choose how to acquire grade: - Work on individual project with 3 milestones. - Take a test on whole semester’s topics

Exam Types:

Test Exam

Compulsory bibliography: Class materials published in Moodle

Recommended bibliography: Hastie, T., Tibshirani, R., Friedman, J. (2009). The elements of statistical learning: data mining, inference and prediction. (https://web.stanford.edu/~hastie/ElemStatLearn/)

Additional bibliography: no

Additional Information: online course