inf040 - Introduction to Data Science (Course overview)

inf040 - Introduction to Data Science (Course overview)

Department of Computing Science 6 KP
Module components Semester courses Summer semester 2024 Examination
Lecture
  • Unlimited access 2.01.040 - Data Science I Show lecturers
    • Prof. Dr. Wolfram Wingerath

    Tuesday: 16:00 - 18:00, weekly (from 02/04/24)
    Thursday: 16:00 - 18:00, weekly (from 11/04/24)
    Dates on Tuesday, 09.07.2024 12:30 - 18:00, Wednesday, 10.07.2024 10:00 - 18:00

    Data Science is an interdisciplinary science at the intersection of statistics, machine learning, data visualization, and mathematical modeling. This course is designed to provide a practical introduction to the field of Data Science by teaching theoretical principles while also applying them practically. Topics covered range from data collection and preparation (data sources & formats, data cleaning, data bias), mathematical foundations (statistical distributions, correlation analysis, significance) and methods for visualization (tables & plots, histograms, best practices) to the development of models for classifying or predicting values (linear regression, classification, clustering).

Exercises
  • Unlimited access 2.01.040 - Data Science I Show lecturers
    • Prof. Dr. Wolfram Wingerath

    Tuesday: 16:00 - 18:00, weekly (from 02/04/24)
    Thursday: 16:00 - 18:00, weekly (from 11/04/24)
    Dates on Tuesday, 09.07.2024 12:30 - 18:00, Wednesday, 10.07.2024 10:00 - 18:00

    Data Science is an interdisciplinary science at the intersection of statistics, machine learning, data visualization, and mathematical modeling. This course is designed to provide a practical introduction to the field of Data Science by teaching theoretical principles while also applying them practically. Topics covered range from data collection and preparation (data sources & formats, data cleaning, data bias), mathematical foundations (statistical distributions, correlation analysis, significance) and methods for visualization (tables & plots, histograms, best practices) to the development of models for classifying or predicting values (linear regression, classification, clustering).

Hinweise zum Modul
Prerequisites

Basics of databases, Python programming and statistics

Prüfungszeiten

At the end of the lecture period or by arrangement with the instructor.

Module examination

Written or oral exam or portfolio or project or practical exercise

Skills to be acquired in this module

The module teaches fundamentals from the field of Data Science, covering purposes, challenges, and common best practices.

Professional competences

The students

  • have knowledge of basic concepts, problems and solution approaches from the field of Data Science.
  • are able to justify the choice of specific data analysis methods for a given problem
  • include possible imponderables in the analysis when evaluating analysis results

Methological competences
The students

  • are able to translate questions from a specific domain into a feasible analysis
  • work on Data Science tasks to expand their understanding of the different approaches and methods.


Social competences

The students

  • discuss approaches and problems encountered in smaller and larger groups

Self competences
The students

  • reflect on their actions when identifying possible solutions and critically question their own results