Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

The Nature of Data

1.024 Aufrufe

Veröffentlicht am

Some notes about data.

Veröffentlicht in: Bildung
  • Als Erste(r) kommentieren

  • Gehören Sie zu den Ersten, denen das gefällt!

The Nature of Data

  1. 1. Nature of data Some notes
  2. 2. New media Lev Manovich
  3. 3. Numerical representation: new media objects exist as data Modularity: the different elements of new media exist independently Automation: new media objects can be created and modified automatically Variability: new media objects exist in multiple versions Transcoding: The logic of the computer influences how we understand and represent ourselves.
  4. 4. Prague case study from our city
  5. 5. 1998 - 2002 Ing. arch. Jan Kasl koalice ČSSD a ODS
  6. 6. 2002 - 2006 Mudr. Pavel Bém koalice ČSSD a ODS
  7. 7. 2006 - 2010 Mudr. Pavel Bém vláda ODS
  8. 8. Data Data is a set of values of qualitative or quantitative variables; restated, pieces of data are individual pieces of information. Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs or images. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage.
  9. 9. Data Mining some notes from “Wiki”
  10. 10. Data A continuous variable is a numeric variable. Observations can take any value between a certain set of real numbers (height, age, temperature, ect..) A discrete variable is a numeric variable that only consist of integers (number of kids, cars, pets,ect...) An ordinal variable is a categorical variable that can be ranked (grades,pizza size,levels of satisfaction) A nominal variable is a categorical variable that can't be ranked (race,religion, sex)
  11. 11. Data mining Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
  12. 12. Data mining Anomaly detection (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
  13. 13. Data mining Association rule learning (Dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
  14. 14. Data mining Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
  15. 15. Data mining Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
  16. 16. Data mining Regression – attempts to find a function which models the data with the least error.
  17. 17. Data mining Summarization – providing a more compact representation of the data set, including visualization and report generation.
  18. 18. A tak dále…
  19. 19. Anscombe's quartet why visualisations?
  20. 20. Thnx @josefslerka