Passing the study plan

The final state examination will consist of both theoretically and practically oriented public dispute focusing on three main parts, checking the following abilities/knowledge:
- explain the main concepts and tools of Mathematics, Statistics, related to Data Science in the frame covered by the Block A of the modules in the list below
- explain main issues covered in the Block C, in particular in relation to practical experience gained during the programme
- discuss the main concepts and tools from one of the specialization modules from the Block E.

Data Analytics is a 7 semester, 210 credit, professional Bachelor's programme for foreign and domestic students, laying out the mathematical foundations of data analytics, machine learning, and appropriate technology, including at least one-semester practicum with a recognized commercial partner.
The proposed study programme is intended to be a single-field professional study programme, but it will include several options for diverse specializations.
The main modules will include (compulsory, if not stated otherwise):
===Block A ===
--- Mathematical foundations – 80 credits in total ---
Mathematics I (discrete and linear algebra-based applications), sem. 1, 12 credits
Mathematics II (1D infinitesimal analysis, including Fourier analysis), sem. 1, 8 credits
Mathematics III (nD infinitesimal analysis and selected more advanced topics), sem. 2, 12 credits
Mathematics IV (discrete mathematics and algorithms), sem. 2, 8 credits
Mathematics V (number theory, algebra, logic), sem. 3, 8 credits
Probability (including necessary measure theory) sem. 3, 8 credits
Statistics (including intensive R practicum) sem. 4, 12 credits
-- Mathematics Advanced Selective - choose one of three:
Optimization and Modelling, sem. 5-6, 12 credits
Numerical Analysis and Scientific Computing, sem. 5-6, 12 credits
Geometric Analysis, 5-6, 12 credits
=== Block B ===
--- Soft skills (general component) – 10 credits in total
Academic and Business Integrity, Writing, and Communication, sem. 2, 5 credits
Project Management, sem. 4, 5 credits
=== Block C ===
--- Programming introduction – 32 credits in total
Introduction to Script Languages (serving also as common extra tutorial to Mathematics I and II), sem. 1, 6 credits
Introduction to Databases, sem. 1, 6 credits
Introduction to Python, sem. 2, 6 credits
Introduction to Object-Oriented Languages, sem. 3, 6 credits
Data Curation and Security, sem. 3, 8 credits
=== Block D ===
--- Bloc Professional experience – 40 credits in total
Applied Data Analytics, sem. 4, 10 credits
Bachelor’s Practice / Capstone – 30 credits, sem. 5-7, 30 credits
=== Block E ===
--- Specialization modules – 48 credits, choose 2 modules out of 4
-- I. Machine Learning and AI – 24 credits
Machine Learning Methods, sem. 5-6, 8 credits
Artificial Neural Networks, sem. 5-6, 8 credits
- Selective – choose 1 of 2:
Natural Language Processing, sem. 5-6, 8 credits
Image Processing and Recognition, sem. 5-6, 8 credits
-- II. Complexity – 24 credits
Network Science, sem. 5-6, 8 credits
Machine Learning and Algorithms on Graphs, sem. 5-6, 8 credits
Modelling Complex Systems, sem. 5-6, 8 credits
-- III. Big Data – 24 credits
Advanced Databases and Big Data, sem. 5-6, 6 credits
Distributed Computing and Cloud Technology, sem. 5-6, 6 credits
Data Visualization, sem. 5-6, 6 credits
Cybersecurity, sem. 5-6, 6 credits
-- IV. Mathematics Advanced Selective – 24 credits by choosing all three options in the selective courses in Block A

The requested one-semester practicum will be related to the credited Bachelor practice / capstone project, where the students will deliver either a technical report, or theoretical work describing their achievements.

not relevant