Data Science

Brief description

  • Get to know different GUIs for Python
  • Distinguish data formats for structured and non-structured data, e.g. data frames, and be able to apply them to real problems
  • Import data
  • Clean data
  • Visualize data
  • Know and be able to use different libraries, such as Numpy, Sympy

Mode of delivery

face to face

Type

compulsory

Recommended or required reading and other learning resources/tools

  • Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data, Packt Publishing (30th September 2021)
  • Data Science from Scratch: First Principles with Python, O'Reilly Media; 2nd edition (16th May 2019)

Planned learning activities and teaching methods

Integrated classes Lectures and exercises

Assessment methods and criteria

Immanent performance assessment (written test, commitment) and written exam.

Prerequisites and co-requisites

Word processing and spreadsheet basics

Infos

Degree programme

Banking and Finance (Bachelor)

Cycle

Bachelor

ECTS Credits

3.00

Language of instruction

English

Curriculum

Part-Time

Academic year

2023

Semester

3 WS

Incoming

No

Learning outcome

After successful completion of the course, the students are able

  • to explain the basics of data science,
  • to help shape them according to their ideas as a link between finance and IT experts,
  • to manage simple inputs and outputs in the Python software,
  • to perform mathematical manipulations on given data using Python
  • to graphically display and highlight desired end results.

Course code

0229-19-01-BB-DE-19