Progression through the study plan
bachelor's full-time single-subject, language of instruction: Czech
Included in the programme: PřF B-SDV Statistical Data Science
Common University Core (15 cr.)
To successfully complete their studies, students in bachelor's degree programmes will receive 15 kr. of courses in the so-called Common University Core. These credits include 2 credits from sports courses (PE), 4 credits for a foreign language, and 9 credits for social science or science foundation courses - the so-called CORE courses. The current CORE course offerings are available here: https://www.muni.cz/studenti/spolecny-univerzitni-zaklad
Social science and science foundation
The student is required to enroll in courses with a minimum total value of 9 credits for the entire Bachelor's degree from the Common University Core. https://www.muni.cz/studenti/spolecny-univerzitni-zaklad
Language Courses
In order to successfully complete studies at the Faculty of Arts, the student is required to take the JASUZ Professional English exam for 4 credits. Language teaching is provided by the MU Language Education Centre.
| Code | Name | Type of Completion | Credits | Term | Specialization |
| PřF:JASUZ | Examination in English for Specific Purposes - Science | zk | 4 | - | - |
| 4 credits |
PE
All full-time students of Bachelor's degree programmes are obliged to complete the requirements for two credits (1 credit = 1 credit) in the sports activities courses listed under P9 during their studies. The courses are provided by the Centre for University Sport of the Faculty of Sport Studies.
Bachelor Thesis (min. 10 cr.)
Compulsory Courses
| Code | Name | Type of Completion | Credits | Term | Specialization |
| FI:MB152 | Differential and Integral Calculus | zk | 3+2 | 1 | Z |
| PřF:M1110D | Linear algebra for data science | zk | 3+2 | 1 | Z |
| PřF:M4130 | Mathematical Software | z | 4 | 1 | - |
| FI:IB113 | Introduction to Programming and Algorithms | zk | 4+2 | 1 | - |
| PřF:M1130 | Mathematical Seminary I | z | 2 | 1 | - |
| PřF:M2100D | Mathematical analysis for data science | zk | 2+2 | 2 | Z |
| PřF:M4180 | Numerical Methods I | zk | 4+2 | 2 | P |
| FI:MB153 | Statistics I | zk | 3+2 | 2 | Z |
| PřF:M1VM01 | Algorithmization and numerical computations | k | 5 | 2 | Z |
| PřF:M4131 | Python for Data Science | z | 4 | 2 | - |
| FI:PV251 | Visualization | zk | 3+2 | 3 | P |
| FI:PB016 | Introduction to Artificial Intelligence | zk | 3+2 | 3 | - |
| PřF:M5120 | Linear Models in Statistics I | zk | 4+2 | 3 | P |
| PřF:M7986 | Statistical inferences I | zk | 4+2 | 3 | P |
| PřF:M8DBR | Database systems and R for data science | zk | 3+2 | 4 | - |
| FI:IB031 | Introduction to Machine Learning | zk | 3+2 | 4 | P |
| PřF:M6130 | Computational statistics | zk | 3+2 | 4 | P |
| PřF:M8DM1 | Data mining I | zk | 4+2 | 4 | P |
| PřF:M7222 | Generalized linear models | zk | 4+2 | 5 | P |
| PřF:M9121 | Time Series I | zk | 4+2 | 5 | P |
| PřF:M9DM2 | Data mining II | k | 2+1 | 5 | - |
| FI:PA153 | Natural Language Processing | zk | 2+2 | 5 | - |
| PřF:M0160 | Optimization | zk | 4+2 | 6 | P |
| 114 credits |
Selective Courses
Students are required to complete selective courses worth at least 25 credits. In total, students must earn 180 credits during their studies. Students can obtain the remaining credits from elective courses at MU or by selecting additional courses from the selective courses.
| Code | Name | Type of Completion | Credits | Term | Specialization |
| FI:IB114 | Introduction to Programming and Algorithms II | zk | 3+2 | 2 | - |
| PřF:E3011 | Algorithmization and programming | k | 4+1 | 2 | - |
| PřF:Bi9680en | Artificial Intelligence in Biology, Chemistry, and Bioengineering | zk | 2+2 | 3 | - |
| PřF:Bi9680enc | Artificial Intelligence in Biology, Chemistry, and Bioengineering - practice | k | 1+1 | 3 | - |
| FI:PB154 | Database Systems | zk | 3+2 | 3 | - |
| PřF:M5180 | Numerical Methods II | zk | 3+2 | 3 | - |
| PřF:M6201 | Non-linear dynamics | zk | 4+2 | 4 | - |
| FI:IV109 | Modeling and Simulation | zk | 3+2 | 4 | - |
| FI:PA026 | Artificial Intelligence Project | k | 2+1 | 4 | - |
| PřF:M6110 | Mathematics of Insurance | zk | 4+2 | 4 | - |
| PřF:M5444 | Markov chains | zk | 3+2 | 5 | - |
| ESF:BPE_ZAEK | Introduction to Econometrics | zk | 6 | 5 | - |
| PřF:M7777 | Applied functional data analysis | z | 3 | 5 | - |
| PřF:M5KPM | Chapters from actuarial mathematics | zk | 3+2 | 5 | - |
| PřF:PLIN068 | Applied Machine Learning | k | 3 | 6 | - |
| PřF:PLIN069 | Applied Machine Learning Project | zk | 6 | 6 | - |
| 74 credits |
Elective Courses
The students are required to complete 180 credits for their studies. They may choose the recommended elective course listed, select from other elective courses at MU, or choose from selective courses within the programme.
| Code | Name | Type of Completion | Credits | Term | Specialization |
| PřF:MPRAX | Professional practice | z | 10 | 6 | - |
| 10 credits |