GRA 6036 Data Analytics with Programming
GRA 6036 Data Analytics with Programming
This course gives an applied introduction to the most important techniques in businessrelated data analytics. Students are given handson experience with programming, working with data, using descriptive statistics to motivate models, and using models to turn data into actionable knowledge. Simulation techniques will be used to assess statistical tools.
Central theory surrounding regression models will be developed. The students will learn applied data analytics and programming using the R software system.
The student will be trained in the extremely flexible R system to do applied data analysis. This includes controlstructures, such as ifstatements and loops, data importation and reorganization, the use of visualization techniques, programming as well as using descriptive statistics, calling upon and implementing some statistical procedures, as well as writing simulation experiments.
Skills in dataselection, datareorganization, datatransformations and descriptive statistics will be developed in connection with datavisualization, model formulation, model diagnostics and model selection will be developed. Skills in choosing and using exploratory tools for getting an overview of large datasets will be developed. Finally, skills in turning a practical question into a question that can be addressed via statistical tools, and then using statistical tools to decide on a course of action for the practical question at hand will be developed.
Through experience in model building and computer experiments, the student will reflect on the limitations of statistical techniques, the issue of subjectivity in reaching statistical conclusions, and the level of trust one may place in statistically based decisions. Further, simulation techniques will be introduced in order to assess the validity of a statistical technique.
 Introduction to R. Introductory descriptive statistics, data visualization and data reorganization. Introductory statistical inference.
 Data exploration and visualization in R.
 An introduction to datamodelling: Simple regression models and an introduction to simulation.
 Multiple linear regression: Dummyvariables, interaction terms, datatransformations and interpretation.
 Regression diagnostics and model selection.

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
All parts of the assessment must be passed in order to receive a final grade in the course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Exam category  Weight  Invigilation  Duration  Support materials  Grouping  Comment exam 

Exam category: Submission Form of assessment: Written submission Exam code: GRA60365 Grading scale: ECTS Grading rules: Internal and external examiner Resit: Examination when next scheduled course  40  No  2 Week(s)  Group (1  3)  Assignment  
Exam category: Submission Form of assessment: Written submission Exam code: GRA60366 Grading scale: ECTS Grading rules: Internal and external examiner Resit: Examination when next scheduled course  60  Yes  3 Hour(s) 
 Individual  Final written examination under supervision 
A course of 1 ECTS credit corresponds to a workload of 2630 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.