Module also offered within study programmes:
General information:
Annual:
2017/2018
Code:
HKL-1-520-s
Name:
Analysis and Visualization of Event Data
Faculty of:
Humanities
Study level:
First-cycle studies
Specialty:
-
Field of study:
Cultural Studies
Semester:
5
Profile of education:
Academic and practical
Lecture language:
English
Form and type of study:
Full-time studies
Course homepage:
 
Responsible teacher:
dr Małecka Anna (amm@agh.edu.pl)
Academic teachers:
prof. Yuskiv Bohdan (yuskivbm@gmail.com)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Skills
M_U001 knows how to use specific data mining methods KL2A_U22, KL1A_U22 Test
M_U002 uses new methods in the R language to perform deep data analysis in the media KL2A_U22, KL1A_U22 Execution of laboratory classes
M_U003 is able to obtain additional information from the data using scientific approach KL2A_U18, KL1A_U18 Execution of laboratory classes
Knowledge
M_W001 has knowledge in data mining analysis, characterizing basics of data processing, describing the data exploration KL2A_W25, KL1A_W25 Test
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Others
E-learning
Skills
M_U001 knows how to use specific data mining methods + - + - - - - - - - -
M_U002 uses new methods in the R language to perform deep data analysis in the media + - + - - - - - - - -
M_U003 is able to obtain additional information from the data using scientific approach + - + - - - - - - - -
Knowledge
M_W001 has knowledge in data mining analysis, characterizing basics of data processing, describing the data exploration + - + - - - - - - - -
Module content
Lectures:

LECTURES:
1. Introduction to data visualization. An overview of visualization types and visualization processes. Stages of visualizing event data (2 hrs.)
2. The Base Graphics system in R. Univariate and multivariate plots. Customizing graphs and saving them in various formats (3 hrs.)
3. The Lattice Graphics in R. Exploring multivariate event data with trellis graphs (2 hrs.)
4. A comprehensive system for graph development with the ggplot2 in R (2 hrs.)
5. Event data and Interactive Graphics in R (2 hrs.)
6. Event data and Google Chart Tools in R (2 hrs.)
7. Case Study: Visualizing Catergorical Data (3 hrs.)

Laboratory classes:

LABORATORY CLASSES (based on examples of the event data visualization)
1. Introduction to R as a statistical programming language. R installation (3 hrs.)
2. The Base Graphics system in R. Univariate and multivariate plots. Customizing graphs and saving them in various formats (3 hrs.)
3. The Lattice Graphics in R. Exploring multivariate event data with trellis graphs (2 hrs.)
4. A comprehensive system for graph development with the ggplot2 in R (2 hrs.)
5. Event data and Interactive Graphics in R (2 hrs.)
6. Event data and Google Chart Tools in R (2 hrs.)
7. Case Study: Visualizing Catergorical Data (3 hrs.)

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 100 h
Module ECTS credits 4 ECTS
Preparation for classes 33 h
Participation in lectures 15 h
Realization of independently performed tasks 33 h
Contact hours 4 h
Contact hours 15 h
Additional information
Method of calculating the final grade:

Subject requirements:
– knowledge of the subject-related basics (statistics and related methods),
– PC user knowledge,
– no more than 15 persons per group,
– Internet access is required.

Prerequisites and additional requirements:

Recommended literature and teaching resources:

Recommended reading
1. Crawley Michael J. The R Book, John Wiley & Sons Ltd 2007
2. Ledolter Johannes Data mining and business analytics with R, University of Iowa, Wiley 2013
3. Maindonald John, Braun W. John Data Analysis and Graphics. Using R – an Example-Based Approach, Cambridge University Press 2003
4. Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson 2005
5. Paradis Emmanuel R for Beginners, Institut des Sciences de l' Evolution 2005
6. Przemysław Biecek, Przewodnik po pakiecie, Oficyna Wydawnicza 2008
7. Statystyczna analiza danych z wykorzystaniem programu R, pod red. nauk. M.Walesiaka, E.Gatnara, Wydawnictwo Naukowe PWN, Warszawa 2013
8. Torgo Luis Data Mining with R: learning with case studies, LIACC-FEP, University of Porto 2003
9. Yanchang Zhao R and Data Mining: Examples and Case Studies, Elsevier 2012
Additional literature
10. Adler Joseph R in a Nutshell, O’Reilly 2010
11. Coghlan Avril A Little Book of R For Multivariate Analysis, https://github.com/avrilcoghlan/LittleBookofRMultivariateAnalysis/raw/master/_build/latex/MultivariateAnalysis.pdf
12. Gatnar E. Podejście wielomodelowe w zagadnieniach dyskryminacji i regresji, PWN, Warszawa 2008
13. Kabacoff Robert I. R in Action. Data analysis and graphics with R, Manning Publications Co 2011 (Кабаков Роберт И. R в действии. Анализ и визуализация данных в программе R, ДМК Пресс, Москва 2014)
14. Larose D.T. Metody i modele eksploracji danych, PWN, Warszawa 2008
15. Pałka Dariusz, Zaskórski Piotr, Data mining w procesach decyzyjnych, Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki, nr 7, Warszawa 2012, s. 143-161
16. Stanisz A. Przystępny kurs statystyki. t III Analizy wielowymiarowe, StatSoft, Kraków 2005
17. StatSoft: Internetowy Podręcznik Statystyki. Techniki zgłębiania danych (data mining), http://www.statsoft.pl/textbook/glosfra.html
18. Venables W. N., Smith D. M. and the R Core Team An Introduction to R. A Programming Environment for Data Analysis and Graphics, 2015
19. Vries Andrie de, Meys Joris R For Dummies, John Wiley & Sons, Ltd 2012

Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

PROGRAMS / SUBJECT TOOLS:
− R
− RStudio
− R packages
Subject results:
– students will get knowledge in data visualization and analysis, characterizing basics of visualization processing, describing the visualizing event data,
– students will use new methods in the R language to perform deep data analysis and visualization of event,
– students will be able to obtain additional information from the data using scientific approach.