Course information

  • Title: Data Exploration and Visualisation
  • Neptun code: dsexplorf17vm
  • Instructor: Dávid Visontai
  • Semester: 2
  • Type: Lecture + Practice
  • Credit points: 4
  • Prerequisites: -

Course description

The aim of the course is that students gain practical skills to access large databases/datasets, to handle data stored in different formats, to explore/distill these data and present/visualise the gathered information. During the course students will come across databases of multiple disciples. Completing of the several projects allows students to gain experience on this field that will be a firm a foundation for later courses on theoretical datamining and advanced computing laboratories.


  • Datatypes, images, timeseries, tables, graphs, textual data
  • Standards of file- and dataformats
  • Raw and processed data, metadata, cleansing of data
  • Developing open source softwares
  • Access data locally and through the web, APIs
  • Access of scientific databases
  • Usage of relational databases
  • Transforming data, sortind, combining
  • Basics of timeseries analysis
  • Basics of imageprocessing
  • Dimension reduction, clustering
  • Infographics, visualisation
  • Interactive dataexplorative tools
  • Reproducible research

Recommended readings

  • Wes McKinney: Python for Data Analysis, (O’Reilly 2013)
  • Joel Grus: Data Science from Scratch (O’Reilly 2015)

Course material