Czech version

Data Analysis II (MADII)

Spring 2022
Course description:
The course is focused on more advanced algorithms of network analysis. The lectures deal with the essence of the individual algorithms in order to be able to assess the suitability of the methods in their usage. In seminars, experiments with selected datasets and tools are performed.

Ongoing activities: 19-36 points

Implementation: 12-24 points

Data analysis: 10-20 points

Written exam: 10-20 points

DATA ANALYSIS II Tasks.pdf

Lectures and Seminars:

Lecture 29.3.2022, Seminar 29.3.2022

Lecture 12.4.2022, Seminar 12.4.2022

Literature and sources:

Slides from lectures

R language, Intro1, Intro2, Intro to igraph package

RStudio

Data for R

Weka

UC Irvine Machine Learning Repository - data, and another data for Weka

Literature
    Ian H. Witten, Eibe Frank , Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques (Third Edition). The Morgan Kaufmann Series in Data Management Systems, 2011. ISBN 978-0123748560.

    Zaki, M. J., Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.

    Bramer, M. (2013). Principles of data mining. Springer.

    Albert-Laszlo Barabasi. Network Science

    Mark Newman. Networks: An Introduction. Oxford University Press, 2010. ISBN 978-0199206650.

Tools for network analysis and visualization

    Pajek - Program for Large Network Analysis, Pajek

    NodeXL - Tempalte for Excel, NodeXL

    SNAP - Stanford Network Analysis Project, SNAP

    Gephi , Graphviz etc.

    Visual Complexity

    D3.js - JavaScript library for manipulating documents based on data, D3.js

Course Outline:

Data for data mining, types and sources of data

Attributes and their types, sparse data, incomplete and inaccurate data

Algebraic and geometric interpretation of data

Probabilistic interpretation of data

Numerical and categorial attributes, the basic analytical approaches

Data mining, pre-processing and data cleaning

Data representation

Foundations of data analysis (classification, clustering)

Networks and their properties

Types of networks and their representation

Basic measures and metrics

Structure and global properties of networks

Basic data structures for network representation

Basic algorithms for network analysis