This document discusses new media and data. It contains the following key points: 1. New media objects exist as numerical data that can be represented, modified, and created automatically in multiple versions. 2. The logic of computers influences how we understand and represent ourselves through transcoding of new media objects. 3. Data is information that is measured, collected, and analyzed, which can then be visualized. Data refers to existing information represented in a usable form.

1. Nature of data
Some notes

2. New media
Lev Manovich

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.

10. Prague
case study from our city

11. 1998 - 2002
Ing. arch. Jan Kasl
koalice ČSSD a ODS

12. 2002 - 2006
Mudr. Pavel Bém
koalice ČSSD a ODS

13. 2006 - 2010
Mudr. Pavel Bém
vláda ODS

14. 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.

15. Data Mining
some notes from “Wiki”

16. 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)

21. 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.

22. 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.

23. 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.

24. 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.

25. 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".

26. Data mining
Regression – attempts to find a function which models
the data with the least error.

27. Data mining
Summarization – providing a more compact
representation of the data set, including visualization
and report generation.

36. A tak dále…

37. Anscombe's quartet
why visualisations?

41. Thnx
@josefslerka