Ricardo Rodríguez Jorge, PhD
Research Scientist
Department of TICS

Technological Centre Ceit/“Researching Today, Creating the Future”
Donosti, Spain

Reseach group: Data Analysis and Information Management Group

Research visit. Institution: National Technologic of Mexico / Technological Institute of Ciudad Victoria.
(July 1st – July 31th 2022)

Visiting Professor
Czech Technical University in Prague, Czech Republic
(Dec 2016 -January 2017)

Research visit
Tohoku University, Japan (May- Jul 2011)


I welcome my colleagues and fellow academics to this web site. If you would like to discuss any of my published work, please feel free to contact me. My professional interests are mainly in Engineering and my work today has been focused on signal processing and machine learning to bridge innovative ways in these areas.
I am always looking for industrial and academic collaboration, please do not hesitate to contact me for project collaborations. For more information about my current running projects please visit Research projects.
Institute web page:  Data Analysis and Information Management Group
Call for papers: https://www.rodriguezricardo.net/rodriguezjorgericardo/
Courses: https://www.rodriguezricardo.net/rodriguezjorgericardo/
Research Interest Group: Mechatronics,  Signal Processing, Control and Artificial Neural Networks
Contact Information: E-mail: rrodriguezj@ceit.es, Telephone:943 212 800 / Ext. 2940, Office: 011, Skype: rodriguezri,  Mobile phone:

Data Analysis and Visualisation

Course Information:
Lecturer                 : Dr. Ricardo Rodriguez Jorge
Course Coordinator :
Level                      : Bachelor course
Time                      : Thrusday (17:00)
Location                 : CPTO Building, 6.14 classroom
(All obligations for a successful assessment have to be fulfilled until the end of the winter semester, 2023)
The course focuses on the presentation of information that are necessary to the basic and comprehensive evaluation of the data. Emphasis is placed on gaining the ability to visualize data by appropriate means. An integral part of the course is the practical application of theoretical knowledge on available data using appropriate software tools (typically Python, R, Matlab, Excel).   
Lecture Notes:
Week Topics Notes Assignments Due date/
1 Introduction to Matlab download Moodle https://portal.ujep.cz/
2 Programming and plotting in Matlab   download Moodle https://portal.ujep.cz/
3 Matrices and matrix operation in Matlab  download Moodle https://portal.ujep.cz/
4 Function of one real variable, numerical differentiation and integration download Moodle https://portal.ujep.cz/
5 Ordinary differential equations    download Moodle https://portal.ujep.cz/
6 Signal and image processing: filtering, transformation (Fourier, wavelets)  download Moodle https://portal.ujep.cz/
7 Introduction to R: data structures, writing functions, control statements, loops, data manipulation, plots etc. download Moodle https://portal.ujep.cz/

Basic concepts of descriptive statistics: methods of data processing, frequency distribution (histogram, polygon)

download Moodle https://portal.ujep.cz/

The statistical analysis of univariate data: moment/quantile measures of central tendency, variability, skewness and kurtosis

download Moodle https://portal.ujep.cz/
10 Statistical analysis of multivariate data: correlation, factor and cluster analysis download Moodle https://portal.ujep.cz/
11 Regression analysis: linear and nonlinear regression models download Moodle https://portal.ujep.cz/
12 Analysis of time series: graphical analysis, decomposition, autocorrelation, trend modeling download Moodle https://portal.ujep.cz/
13 Summary of selected techniques of static and dynamic visualization  download Moodle https://portal.ujep.cz/