Data Analytics & Visualization
Niveau
second cycle, Master
Learning outcomes of the courses/module
The graduate / student:* 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 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
Share of e-learning in %
20
Semester hours per week
2.5
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
Name of lecturer
Dipl.-Ing. Christoph Fröschl
Academic year
Key figure of the course/module
DAT.3
Type of course/module
integrated lecture
Type of course
Compulsory