Data Science

Brief description

  • Introduction to the various areas of Data Science
  • Overview of the programming languages R and Python
  • Statistical Learning
  • Use of R for data cleansing with Big Data
  • Data visualization
  • Overview of Deep Learning

Mode of delivery

face to face

Type

compulsory

Recommended or required reading and other learning resources/tools

Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining), 1st edition, Larose, 2019

Planned learning activities and teaching methods

Integrated course (ILV), Lecture, exercises, quizzes, group work, presentation, discussion

Assessment methods and criteria

Continuous assessment (quality of the work assignments and objectives completed by the students, presentations; 2 written quizzes) and written exam

Prerequisites and co-requisites

Business Informatics

Infos

Degree programme

Banking and Finance (engl.)

Cycle

Bachelor

ECTS Credits

3.00

Language of instruction

English

Curriculum

Full-Time

Academic year

2025

Semester

3 WS

Incoming

No

Learning outcome

After successful completion of the course, students can

  • identify the methods for extracting useful information from raw data
  • define their areas of application
  • select tools for data exploration and data mining
to apply them to raw financial data
  • name various areas of data science and distinguish them from each other

Course code

1229-19-01-VZ-EN-19