Ricardo Rodríguez Jorge, PhD
Assistant Professor/Researcher
Faculty of Science
Department of Informatics

Jan Evangelista Purkyně University
Czech Republic

Mechatronics,  Signal Processing, Control and Artificial Neural Networks
Research Interest Group
Visiting Professor,
Czech Technical University in Prague, Czech Republic
(Dec 2016 -January 2017)

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


Contact Information:
E-mail:
ricardo.rodriguezjorge.mx@ieee.org
Telephone:
+420 475 286 724
Office: 6.07  (CPTO)
Skype: rodriguezri

 

                                     
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: https://ki.ujep.cz/cs/personalni-slozeni/ricardo-rodriguez-jorge/
 

Python and R for Data Science

<< TO BE UPDATED, PLEASE VISIT AGAIN >>
Course Information:
Lecturer                 : Dr. Ricardo Rodriguez Jorge
Course Coordinator : RNDr. Jiří Škvor, Ph.D.
Level                      : Bachelor course
Time                      : Friday, Saturday
Location                 : CPTO Building
 
Announcement:
(All obligations for a successful assessment have to be fulfilled until the end of the winter semester, 2021)
Sylabus:
In the course, students will practically develop basic skills in programming languages Python and R in key areas for data engineers and scientists. Students will learn various methods and techniques of data processing, analysis and visualization purely practically on model solutions, ie at the application and interpretation level, without the need for deeper knowledge of the principles of these methods and techniques, which should be acquired in previous or next study. A significant part of the teaching is the work of students in groups on solving case studies ("inspired by data") of a smaller scale, their presentation and mutual critical evaluation. Kaggle.com platforms are a source of data and inspiration. The materials of teaching platforms such as datacamp.com will be used in teaching, which are otherwise recommended especially for self-study and obtaining certificates.  
 
Lecture Notes:
 
Week Topics Notes Assignments Due date/
Remarks
1

Deepening the basics of syntax and basic constructions of Python and R languages 

download
         
2 Basics of working with data and data files and their visualization  download    
3 Advanced techniques for working with data and data files (import, data cleaning, etc.) download    
         
4 Advanced data visualization techniques  download    
5 Exploratory data analysis, selected advanced statistical methods (correlation, regression analysis, factor, cluster analysis, etc.), inference statistics  download
         
6 Exploratory data analysis, selected advanced statistical methods (correlation, regression analysis, factor, cluster analysis, etc.), inference statistics download    
         
7 Basic applications of machine learning methods (selected classifiers or algorithms for regression and clustering)  download    
         
8

Basic applications of machine learning methods (selected classifiers or algorithms for regression and clustering)

download    
         
9

Basics of text analysis, sentiment analysis

download    
10 Network analysis download    
         
11 Reports, dashboards and interactive data visualization download    
         
12 Reports, dashboards and interactive data visualization download    
         
13 Summary, discussion of assignment of seminar papers  download