Course: English for Mathematicians and Data Analysts

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Course title English for Mathematicians and Data Analysts
Course code KMA/OAJ
Organizational form of instruction Seminary
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 2
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Kuřil Martin, RNDr. Ph.D.
Course content
unspecified

Learning activities and teaching methods
unspecified
Learning outcomes
The aim of this course is to develop the language skills of mathematics and data analysis students, focusing on professional communication in English. Students will learn how to present their field of study, abilities, and skills, including writing a CV and a cover letter. They will work with specialized texts in mathematics, statistics, and IT, identifying key ideas, preparing presentations, and creating posters. Emphasis is placed on the correct naming of mathematical objects and statistical graphs, reading and writing mathematical formulas and theorems, as well as understanding the differences between Czech and English typography. Students will also enhance their ability to effectively use AI tools when working with specialized texts.

Prerequisites
unspecified

Assessment methods and criteria
unspecified
Active participation in seminars and completion of assigned tasks.
Recommended literature
  • Anglický jazyk pre študentov matematiky. .
  • MIT OpenCourseWare. .
  • AXLER, Sheldon. Linear Algebra Done Right. .
  • BUBENÍK, František. Mathematics for engineers. Praha: České vysoké učení technické, 2021. ISBN 978-80-01-06877-9.
  • HOSSEIN PISHRO-NIK. Introduction To Probality, Statistics and Random Processes. .
  • KŘEPINSKÁ, Alexandra; BUBENÍKOVÁ, Miluša a MIKULÁŠ, Martin. Angličtina (nejen) pro studenty MFF UK. Praha: Matfyzpress, 2019. ISBN 978-80-7378-383-9.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester