Nature of dataSome notes

New mediaLev Manovich

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.

Praguecase study from our city

1998 - 2002Ing. arch. Jan Kasl

koalice ČSSD a ODS

2002 - 2006Mudr. Pavel Bém

koalice ČSSD a ODS

2006 - 2010Mudr. Pavel Bém

vláda ODS

DataData 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.

Data Miningsome notes from “Wiki”

DataA 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)

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.

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.

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.

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.

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".

Data mining

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

Data mining

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

A tak dále…

Anscombe's quartetwhy visualisations?

Thnx@josefslerka