[ 971MECOPDVK18 ]                                         KS                                         KS Programming, Data Management and Visualization
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                | Es ist eine neuere Version 2019W dieser LV im Curriculum Master's programme Social Economics 2021W vorhanden. | 
                
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                  | (*)  Unfortunately this information is not available in english. | 
                
                                
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                      | Workload | 
                                            Education level | 
                      Study areas | 
                                            Responsible person | 
                                                                  Hours per week | 
                                            Coordinating university | 
                     
                    
                      | 4 ECTS | 
                                            
                      M1 - Master's programme 1. year | 
                      Economics | 
                                                                  
                          Alexander Ahammer                       | 
                                               
                                            2 hpw | 
                                            Johannes Kepler University Linz | 
                     
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                      | Detailed information | 
                     
                                        
                      | Pre-requisites | 
                      (*)Zulassung zum MA Studium
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                      | Original study plan | 
                      Master's programme Economics 2018W | 
                     
                      
                    
                      | Objectives | 
                      Students learn advanced programming in statistical software (R and STATA), as well as management and visualization of big data (e.g., administrative data).
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                      | Subject | 
                      Programming of functions and unique statistical methods; merging and preparing big data sets; cleaning and analysis of data.
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                      | Criteria for evaluation | 
                      Regular home work assignments
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                      | Methods | 
                      Lecture by instructor and discussion of home work assignments by students
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                      | Language | 
                      English | 
                     
                      
                    
                      | Study material | 
                      Wickham, H. and Grolemund, G. (2017), R for Data Science, O’Reilley.
Matloff, N. (2011), The Art of R Programming, No Starch Press
Further references will be announced in the first lecture.
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                      | Changing subject? | 
                      No | 
                     
                      
                    
                     
                    
                    
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                      | On-site course | 
                     
                         
                    
                        | Maximum number of participants | 
                      200 | 
                          
                    
                      | Assignment procedure | 
                      Assignment according to priority | 
                     
                    
                     
                    
                    
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