GRA 6036 Data Analytics with Programming

GRA 6036 Data Analytics with Programming

Course code: 
GRA 6036
Department: 
Economics
Credits: 
6
Course coordinator: 
Steffen Grønneberg
Benny Geys
Course name in Norwegian: 
Data Analytics with Programming
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2024 Spring
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

This course gives an applied introduction to the most important techniques in business-related data analytics. Students are given hands-on 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.

Learning outcomes - Knowledge

Central theory surrounding regression models will be developed. The students will learn applied data analytics and programming using the R software system.

Learning outcomes - Skills

The student will be trained in the extremely flexible R system to do applied data analysis. This includes vectorial data operations, 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 data-selection, data-reorganization, data-transformations and descriptive statistics will be developed in connection with data-visualization, 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.

General Competence

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.

Course content
  • Introduction to R. Introductory descriptive statistics, data visualization and data re-organization. Introductory statistical inference.
  • Data exploration and visualization in R.
  • An introduction to data-modelling: Simple regression models and an introduction to simulation.
  • Multiple linear regression: Dummy-variables, interaction terms, data-transformations and interpretation.
  • A brief introduction to regression diagnostics and model selection.
Software tools
R
Additional information

It is the student’s own responsibility to obtain any information provided in class.

Qualifications

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.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Written submission
Weight: 
40
Grouping: 
Group (1 - 3)
Duration: 
2 Week(s)
Comment: 
Assignment.
An oral defense of the assignment might be required.
Exam code: 
GRA 60365
Grading scale: 
ECTS
Resit: 
Examination when next scheduled course
Exam category: 
Submission
Form of assessment: 
Written submission
Invigilation
Weight: 
60
Grouping: 
Individual
Support materials: 
  • BI-approved exam calculator
  • Simple calculator
  • Bilingual dictionary
Duration: 
3 Hour(s)
Comment: 
Final written examination under supervision
Exam code: 
GRA 60366
Grading scale: 
ECTS
Resit: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36
Lectures and blended learning with projects for students.
Teaching
10 Hour(s)
Project-work under supervision in smaller classrooms.
Examination
15 Hour(s)
Work related to the home exam.
Examination
3 Hour(s)
Final exam
Student's own work with learning resources
96 Hour(s)
Sum workload: 
160

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.