GRA 4110 Applied Data Analytics
GRA 4110 Applied Data Analytics
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. Programming, mathematical theory and applications will be interwoven in application focused projects.
Central theory surrounding regression models will be developed. The students will learn applied data analytics and programming using the R software system. Skills in working with high technical precision will be developed.
The student will be trained in the extremely flexible R system to do applied data analysis. This includes controlstructures, loops, data importation and reorganization, the use of visualization techniques, and some general programming, as well as using descriptive statistics, calling upon and implementing some statistical procedures, as well as writing simple 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.
Skills in technical and mathematical understanding will also be developed, mainly through work on mathematical problems related to the applied projects, focusing on the interplay between arithmetical rules, data access, and implementation of computer algorithms.
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 and out of sample forecasts will be introduced in order to assess the validity and quality of a statistical technique.
 Introduction to sums and summation notation, and other foundational issues.
 Introduction to R. Introductory descriptive statistics, data visualization and data reorganization. Data exploration and visualization in R
 A/B testing, and a review of statistical inference via testing for a proportion (exact theory), a brief review of large sample inference, and an introduction to the bootstrap
 An introduction to datamodelling: Simple regression models and an introduction to simulation.
 Multiple linear regression: Dummyvariables, interaction terms, datatransformations and interpretation.
 Simulation and out of sample forecasts.
 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 get a 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: GRA 41101 Grading scale: ECTS Grading rules: Internal and external examiner Resit: Examination when next scheduled course  40  No  1 Week(s)  Group/Individual (1  3)  Assignment An oral defense of the assignment might be required.  
Exam category: Submission Form of assessment: Written submission Exam code: GRA 41102 Grading scale: ECTS Grading rules: Internal and external examiner Resit: Examination when next scheduled course  60  Yes  3 Hour(s) 
 Individual 
Activity  Duration  Comment 

Teaching on Campus  36 Hour(s)  Lectures, including time for project work (blended learning). 
Teaching on Campus  12 Hour(s)  Project work under supervision, in class rooms 
Examination  15 Hour(s)  Work with home exam 
Examination  3 Hour(s)  Final exam 
Student's own work with learning resources  94 Hour(s) 
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.