Data Analytics & Visualization
Niveau
second cycle, Master
Learning outcomes of the courses/module
The participants:
• can describe the contents, results/applications and working methods of Data Science
• can convert "questions" into requirements in the context of Data Science
• can define the process and tools based on these and implement / use them
• knows a software with libraries for implementing data analysis and evaluation
• can use appropriate software
• can carry out suitable evaluations and analyses using the software for defined examples
• can describe the contents, results/applications and working methods of Data Science
• can convert "questions" into requirements in the context of Data Science
• can define the process and tools based on these and implement / use them
• knows a software with libraries for implementing data analysis and evaluation
• can use appropriate software
• can carry out suitable evaluations and analyses using the software for defined examples
Prerequisites for the course
none
Course content
• Introduction (data, information, knowledge, temporal components, objectives)
• Data process (collection, preparation, analysis, presentation)
• Data preparation (cleansing, transformation, rescaling, storage)
• Approaches for the analysis of data
• Presentation/visualization of results
• Software (open source and proprietary software)
• Machine Learning - process, approaches, implementation
• Introduction to the software used e.g. Python
• Collecting and preparing data using software
• Analysis and presentation of sample data using various approaches (e.g. regression, decision trees, etc.)
• Data process (collection, preparation, analysis, presentation)
• Data preparation (cleansing, transformation, rescaling, storage)
• Approaches for the analysis of data
• Presentation/visualization of results
• Software (open source and proprietary software)
• Machine Learning - process, approaches, implementation
• Introduction to the software used e.g. Python
• Collecting and preparing data using software
• Analysis and presentation of sample data using various approaches (e.g. regression, decision trees, etc.)
Recommended specialist literature
• Runkler Th.; Information Mining; vieweg; 2000
• Langit L.; Smart Business Intelligence Solutions with Microsoft SQL Server; Microsoft Press; 2008
• Petersohn H.; Data Mining; Oldenbourg; 2005
• Provost F., Fawcett T.; Data Science for Business; O’Reilly; 2013
• Milton M.; Head First Data Analysis; O’Reilly; 2009
• Langit L.; Smart Business Intelligence Solutions with Microsoft SQL Server; Microsoft Press; 2008
• Petersohn H.; Data Mining; Oldenbourg; 2005
• Provost F., Fawcett T.; Data Science for Business; O’Reilly; 2013
• Milton M.; Head First Data Analysis; O’Reilly; 2009
Assessment methods and criteria
exam
Language
English
Number of ECTS credits awarded
5
Semester hours per week
Planned teaching and learning method
Lecture, individual work with software, group work, presentation and discussion of tasks
Semester/trimester in which the course/module is offered
3